2nd International Workshop on Structural Health Monitoring for Railway System (IWSHM-RS 2018)
The Electric Field Intensity and Potential Distribution Analysis of the Pantograph and Its Supporting Insulators of Electrical Multiple Unit
The high voltage system of an EMU (electrical multiple unit) is an important part of the power supply, and its electrical safety is directly related to the safe and stable operation of the EMU. With the increasing speeds of EMUs, the high-voltage equipment of the EMU should not only sustain the normal working voltages of the contact network, but also withstand the impact of overvoltage. The transient overvoltage generated during the operation of the EMU and the lightning overvoltage generated during thunderstorms may cause insulation breakdown of the electrical equipment on the roof of carriage, accelerate the insulation aging process, and affect the normal operation of the vehicle's high-voltage equipment. In this paper, the electromagnetic field analysis software Ansoft Maxwell is used to model and analyze the electric field intensity and potential distribution of the pantograph and its supporting insulators of the EMU. The distribution of the electric field intensity and potential of the pantograph and its supporting insulators under normal operating voltage, transient voltage and lightning overvoltage is studied to understand the weak links of insulation coordination of the high-voltage components, optimize the connection structure mode and the layout of high-voltage components. This is important to ensure the real-time security and efficient operating life of the high voltage system of an EMU.
Study on Application of Automatic Track Gauge Changeover System in Europe
For the history reason there are many different track gauge in the world, they can be sorted to three categories for stand, border and narrow. How trains overcome from one track gauge to another safely and conveniently, for example from border gauge 1,520 mm to stand gauge 1,435 mm, is an important technical issue for railway engineers worldwide. Initially using the lifting jack to exchange bogie, than an idea of overcoming atomic by combined adjustable gauge wheelset and changeover facility came true, railway industry is striving to improve security and efficiency for changeover track gauge approach. In this paper, there is much focus on the application of the existing four systems in Europe include Germany, Spain and Poland. Due to the different design perception the four systems are not compatible with each other, especially the system in Germany is only applied for freight. Spain is playing a dominant role in this field for its major commercial operating in passenger traffic and has developed an evolution of the fourth generation in a row. Through the comparative assessment of the European systems, it is available to make some conclusion about the technical develop direction. Although the different systems operate separately, the basic principle remains the same. There are institutes and companies in the railway sector are encouraging to develop unique facility which is interoperable between different gauge tracks.
Study on the Cooperative Control Strategy of the WSP Between the ED Brake and the EP Brake
At present, high-speed EMU and intercity EMU often use the combined braking mode of the ED brake and the EP brake in the case of service brake and emergency brake. This paper starts with the wheel flats during the actual operation of the EMU, and through data analysis, studies and summarizes the cooperative control strategy of the WSP between the ED brake and the EP brake under the conditions of low adhesion, and proposes and optimizes the cooperative control strategy of the WSP between the ED brake and EP brake.
The Application of Laser Projection Technology in High-speed Railway
With the continuous development of advanced manufacturing technology, automation, robot, and the further promotion of laser technology, brought a new method to assembly process. In the past, the component of assembly process need to sample numbers, large volume, carrying heavy, more staff, more time and vigor, seriously restricted the production efficiency. Though the introduction of new technology, reasonable application of automation and laser technology can eliminate the sample, reduce the number of operators and improve the production efficiency.
Impact of Tool Pin Configuration on Weld Formation of Non-thinning Friction Stir Welded Aluminum Alloy
Non-thinning friction stir welding (NT-FSW) is the critical technique to guarantee the accurately shaped welding of structures in rail industries. In order to illuminate the affecting characteristic of tool pin configuration on NT-FSW behavior and thus to provide technological guidance for the high-quality applications of the process, the welding tools with smooth pin, smooth-flattened pin and threaded-flattened pin are utilized to conducted NT-FSW experiments in the present paper, and the weld morphology and the defect distribution are comparatively investigated on aluminum alloy welds. The results indicate that for NT-FSW process the thread feature is the most important to the tool pin for the formation of defect-free joints. The flattened tool pin widens the softening region and improves the softening levels at the weld top. By contrast, the thread feature can control the local softening occurring in FSW, which narrows the softening region width.
The Application of Auxiliary Diagnostic Device in the High Speed Train
The paper puts forward a new type electric cabinet auxiliary diagnostic device applied in high speed train. The device mounted in electric cabinet, depending on on-board network, real- time monitoring the status of key electric circuit in electric cabinet. In hardware and software design, the device systematically designs multi-parameter acquisition and decision-making mechanism, in a variety of ways such as hardware acquisition, software calculation, logical configuration, can effectively improve the efficiency of electric fault pre-diagnosis.
Study and Application on Wire-break Defect of Converter Module of HXD1 Locomotive
For the purpose of improving the reliability of electric locomotive’s traction control system, and solving the problems of wire’s mass fracture. Through researching the converter module’s driving board wire-break phenomenon of HXD1 locomotive, based on the module’s vibration analysis on locomotive, introduced an easy and new measure to slow the fatigue of wire connection. For the first time by using the measured real road spectrum to simulate vibration test in the actual road conditions, by the contrast of the reliability of several wire-dealing ways, further we developed heat-shrinkable tubular terminals to solve the problem of harness break caused by vibration and fatigue. Now this kind of skill had been massively popularized on other products like metro, mine truck and high-speed train, The connection reliability of the traction control system are encouraged to increase to over 25 years.
Study on Improving the Efficiency of Bolt-coated Assembly Glue Based on TRIZ Theory.
In the manufacturing process of high-speed EMU, railway vehicles and urban rail vehicles, a large number of bolts are used with assembly glue in order to prevent the failure of bolts. This article has explained systematically how to obtain the methods to improve the efficiency of bolt-coated assembly glue via TRIZ theory, and a series of evaluations and improvements have been made. Finally, the efficiency of bolt-coated is greatly improved, and the result of error-proofing is pretty good.
Failure Analysis and Countermeasures of the WSP Control System in High-speed EMU
At present, high-speed EMU still use adhesive braking as the main braking method. Inevitably, there is a risk of sliding during the braking process. As the EMU speed increases, the adhesion coefficient between wheel sets and rails decreases, and the probability of wheel slide increases. Based on the analysis of slide incident of high-speed EMU under extreme weather conditions. This paper studies the cooperation mechanism of the WSP between the electro-dynamic(ED) brake and the electro-pneumatic(EP) brake under the low-adhesion condition. The judgments of slide and the WSP logic. Proposes solution to avoid slide incidence under low adhesion in extreme weather conditions.
Manufacturing Risk Analysis of Stamping Parts Based on Stainless Steel Urban Railway Car Laser Welding
The laser welding technology used in the steel structure manufacturing of the stainless steel urban railway car requires almost no assembly gap to weld, which puts forward a new subject for stamping parts accuracy and welding impacting fixture. In order to satisfied the laser welding technology almost 0 assembly gap requirement to stamping parts, according to the structure of stamping parts and the condition of welding impacting fixture, this paper analysis and researches the risks of the structure of stamping parts, tolerance direction, process methods, detection methods, quality management and other aspects, and optimizes the technology to ensure the stamping parts’ assembly gap involved in laser welding is minimum by controlling the dimension and the geometric tolerance accuracy, such as the parts’ main point, line or flat, which effectively ensure the quality of laser welding.
Development of the 1700V Hybrid SiC Modules.
IGBT has the characteristics of high output power and high switching frequency, which has become the best choice for high voltage and high power switches. As a single carrier device, The sic JBS has high switching frequency due to its no reverse recovery characteristics. In this paper, a hybrid sic module with SiC JBS instead of Si diode in IGBT module is proposed. By comparing the test results, it is found that the hybrid module has obvious advantages in diode switching loss and IGBT turn-on curve. Compared with the traditional pure Si module, the switching speed of the hybrid module can be faster. When the hybrid module is turned on, there is no current spike and voltage spike when the diode is turned off, so the reliability of the hybrid module is improved. By calculation, the total power loss of the hybrid module on the diode is only 38.3% of that of the traditional module under the same operating conditions, which means that the hybrid module is more energy efficient and generate less thermal
The Application of Cable Shielding Protection Technology in High-speed EMU Manufacturing Artwork
In recent years, China has came into the era of High-speed. In the High-speed EMU, the traction power is stronger, and the space distribution is wider, which also cause the facts that the internal electromagnetic environmental deteriorates seriously, data transmission errors may occur once the undesired signals disturb the weak current control and communication appliances inside, and finally threaten the safety running of High-speed EMU. Therefore, more strict requirements related to the telecommunication cables are put forward in the High-speed EMU manufacturing process. This paper, after carrying out lots of effective analysis on the generation and inhibition of the capacitive coupling noise, the inductive coupling noise and the electromagnetic wave around the electric wires, gives some examples and then introduces the working principle and application of the cable shielding protection technology to achieve the purpose of reducing the electromagnetic interferences.
Weight Sensing Device for Railway Heavy-duty Freight Car
The objective of the paper is to introduce a design to weigh the goods automatically on a railway heavy-duty freight car. In the research described here, a kind of weight sensor is designed in the way to replace the adapter so that it can weigh the load on the bearing which is mounted to a wheel set. In this way the goods on the heavy-duty freight can be obtained automatically.
The Quantitative Analysis of Trail-wheel Calibration Function
Safety is an important index of railway signaling system. Therefore, with the development of urban railway interoperability, the safety comparison becomes critical in selecting deferent function implementation strategies for railway signaling system. Among these safety functions, correctness and safety of train-wheel calibration is the foundation of consecutive train supervision and control functions. And train-wheel calibration strategies selection directly influences the following safety functions. This study conducts quantitative analysis of train-wheel calibration function and its RAMS influence on following train operation by establishing mathematic model. By comparing different analysis results, it provides solid selection suggestions for different train wheel-diameter calibration strategies.
Magnetic Shielding Performance of Railway Vehicle Moving Detection System Based on Magnetoresistive Effect
This present paper studied Magnetic shielding performance of Railway vehicle moving detection system Based on magnetoresistive principle. Based on analyzing the present research situation of train dynamic offset detection at home and abroad, and the detection principle and method based on electromagnetic theory. Based on magnetoresistive effect , this paper put forward the system model of train dynamic offset and discussed the interference form from external magnetic field, proposed the use of shield to eliminate external magnetic field interference. Magnetic shielded finite element model was established by ANSYS software, and the rationality and accuracy of the simulation were determined by grid independent analysis. In this design, the change of the distance between the interference pole and the test point, the change of the distance between the test point and the side wall of the magnetic shield, and the relative position change between the interference pole and the open side of the opening shield is simulated. The results show that the magnetic shield can effectively suppress the interference of external magnetic field , shielding mask magnetic shielding performance in the 0.1G and below the accuracy of the magnetic shielding performance is good.
Research of Attenuation Characteristics of Bolt Tightening Torque for Rail Vehicle
Rail transit vehicles mostly rely on welding connection technology. However, bolt connection is still used in the installation of hoisting equipment and detachable parts of vehicle. In this paper, the attenuation mechanism of bolt tightening torque is described, and it is concluded that the attenuation of bolt tightening torque can be represented by the attenuation of pretightening force when the friction coefficient remains constant at first. Then the M16×100 bolt of A2-70 material is taken as the research object. The distribution of the actual initial pretightening force under different tightening torques and the decay curve of the pretightening force under axial alternating load are studied through experiments. Based on the above, through a series of tests, the variation law of the bolt pretightening force attenuation was studied using transverse alternating load under different bolt tightening torque, amplitude and loading frequency. The results show that the greater the initial pretightening force of the bolt, the slower the slowdown of the pretightening force. Under the action of the transverse alternating load, the effect of the vibration amplitude on the pretightening force of the bolt is more significant than the frequency. And the value of the vibration amplitude can affect the final attenuation of the bolt pretightening force. The value of the vibration frequency more influences the initial decay speed of the pretightening force. The research results provide reference for stability requirements of bolt quality and selection design of bolts for rail transit vehicle.
Risk Analysis and Safety Strategy of Loss of Magnetism in Permanent Magnet Motors of Rail Vehicles
Due to the high efficiency, small size, light weight, reliable operation and low noise, permanent magnet motors have attracted extensive attention and application of rail vehicles. However, due to the inherent characteristics of the permanent magnet materials, the permanent magnet motor has a risk of loss of magnetism. In this paper, the risk factors that may cause the loss of magnetism of permanent magnet motor are analyzed from the aspects of high temperature, external magnetic field, oxidative corrosion, vibration and shock. Combined with the characteristics of permanent magnet motor application in rail vehicles, a safety strategy to avoid the risk of loss of magnetism is proposed. It lays a foundation for the research of anti-magnetism prevention in different stages of design, manufacturing, operation and maintenance of permanent magnet motor.
A Novel 3D Laser System that Can Emit and Measure Guided Wave for Monitoring Rail Track Structural Integrity
Laser-generated guided wave (GW) is an emerging NDT technology in recent year. This paper reports the use of laser to emit GW and measure its reflected signal for monitoring the integrity of train rails. The inspection system is a completely non-contact type of laser system. A Nd: YAG laser acted as an emitter to emit a broadband guided wave onto the inspected rail. The propagation of GW along the rails was then measured by a 3D Laser scanner. Experiments were conducted on a healthy rail and a defective rail. The GW’s interactions with rail structure were recorded. Two interesting observations have been found. First, the defective area will be excited by the continuous emission of laser pulses. This area will generate higher displacement and vibration than that of the other healthy areas of the rail. Second, when the incident wave hit the defective area, the incident wave becomes much wider suddenly. These two phenomena can be used to distinguish defective and healthy areas of the rail during inspection. The above results are the first time reported here and demonstrated the effectiveness of this 3D laser system in the capability of rail structural health monitoring.
Study on Modal Parameters Identification of Proportional Body of High-speed Trains
In view of the high cost and long period of research on modal vehicle modal test, this paper proposes a research method for modal parameter identification of vehicle body structure through proportional vehicle body. The m+p VibPilot vibration test system was used for data acquisition and post-analysis processing. Based on the modal parameter recognition theory, the first three-order elastic modal parameters of the proportional car body were acquired using the Poly Max modal extraction method. The results show that the frequency of diamond, first-order vertical bending and firstorder transverse bending modes are 82.65Hz, 211.9Hz and 222.5Hz, respectively. The vibration characteristics of the proportional vehicle body are evaluated, which provides a basis for the optimal design of the car body structure.
Research on Reliable High-speed Train Axle Temperature Monitoring System based on Fluorescence Optical Fiber Temperature Sensor
The platinum resistance temperature sensor which is widely used in high speed train has the problem of reliability, and usually caused train delay. This paper presents a reliable high-speed train axle temperature monitoring system based on fluorescent optical fiber temperature sensor, and it has the advantages of accurate temperature measurement, good versatility, simple principle, and low cost. The fluorescent decay effect is used to measure the temperature, and the optical fiber is used to transfer the fluorescent signal, in order to overcome the failure of insulation, water, and electromagnetic interference (EMI). The system shows good accuracy and reliability in the type test, and will be used on the track engineering vehicle to get a real test.
Ball-groove Contact Identification Using Ultrasound Reflectometry
This paper presents a way to identify the contact position of a steel ball over a steel plate with a machined groove, by using the ultrasound reflectometry in both static and dynamic manner. The static test was carried out with an ultrasonic probe scanning over the contact region of a steel ball in contact with a line of groove on a rectangular plate, and the dynamic test was conducted using an ultrasonic array revealing contact information of three steel balls rolling over a ring of groove on a rotating plate. This approach provides a new thinking of dynamic condition monitoring for machine elements, especially for ball bearings in high-speed train bogie and wheel-rail contact systems.
Design and Integration of Maglev Platform Mobile Detection System Based on Multi-sensors
With the rapid development of urban rail transit, maglev train is gradually applied to daily transportation due to its fast speed, low energy consumption, high punctuality, and other advantages. Detecting the maglev boundary and obstacle is very important for the safety and smooth operation of the maglev train. Currently, there is no developed technology for maglev detection; the traditional manual detection method is low efficiency. Laser profile scanners can obtain the point cloud of the maglev cross-section and can be used to construct maglev track models. In this study, the mobile detection system on maglev platform was designed and integrated based on three laser profile scanners, an inertial measure unit (IMU) equipped with a GNSS receiver antenna, a GPS clock, a router. In order to complete multi-sensors data fusion to get the three-dimensional point cloud, the time synchronization, calibration and spatial synchronization were finished, the integrated maglev platform detection system based on multi-sensors can get high precision point cloud data surrounding the track, and can be used to detect the boundary and obstacle such as trees, human being and other weird materials in the range of maglev dynamic way, ensuring the safety of train operation.
Development of High-speed Maglev Train and Track Multi-channel Synchronous Testing System
This paper presents a multi-channel, high precision and low cost test system for maglev train and track monitoring based on LabVIEW. The hardware platform of the testing system is built and the corresponding software development is completed. The system has many advantages, such as multi-channel, small volume and low cost. At the same time, the effectiveness of the test system is verified by field measurement of the maglev running line in Shanghai.
Introducing ATOMS—Active Train Online Monitoring System
An effective onboard structural health monitoring (SHM) system- ATOMS, is proposed to reshape traditional scheduled maintenance practices for condition-based maintenance procedures to enhance train safety and to reduce the cost of maintenance. A complete train lifecycle safety management program is envisioned to involve data collection, data processing, information dissemination, data and information storage, and operational decision management and control. To address the requirements, ATOMS - Active Train Online Monitoring System is developed as a complete SHM solution for the high speed Electric Multiple Units (EMU) applications. ATOMS provides: (1) real-time health-centric operation; (2) on-demand structural health diagnostics; (3) diagnostics-assisted prognostics; and (4) proactive maintenance.
The Influence of Electromagnetic Wire Gauge of Frequency Conversion Motor on the Interturn Electric Field
With the increase of the turn short of the variable frequency traction motor, the optimization of interturn insulation structure becomes a necessary subject. This paper is based on a variable frequency traction motor with a power of 350kW, 1800r/min and rated voltage of 2800V, by studying the influence of the interturn thickness and the chamfering Angle on the electric field. At the same time, the life analysis of corona film and ordinary film is analyzed. Through the finite element analysis and related experiments, the interturn electric field of different chamfering Angle of the motor is obtained. Guide the film material selection of the motor to ensure the reliability of interturn insulation.
Rail Crack Detection Using Adaptive Threshold and Wiener Filter Method Based on Acoustic Emission Technology
As a new method to detect rail cracks, acoustic emission (AE) technology has the advantages of dynamic crack detection, unlimited geometric shape, high sensitivity and real-time online monitoring. However, it is easily contaminated by the noise signals which restrain the application of AE technology. In order to solve this problem, this paper adopts the adaptive threshold method and Wiener filter to study the denoising method. For adaptive threshold method, the optimal time window is obtained from the intersection of confidence intervals (ICI) method. Based on the prior information of the signal, the Wiener filter coefficients are calculated. The Wiener filter is used for signal preprocessing. And then the adaptive threshold denoising method is used to detect crack signal. The simulation crack signals are used to analyze the improved method. It is also verified by the AE signals acquired from the experiment in real operating environment. The results clearly illustrate that the proposed method is effective. It can provide a useful guidance for AE detection of rail cracks.
The Application of Self Correlation Function-cyclic Spectrum Analysis Method in Bearing Fault Diagnosis
At present, the research of rolling bearing fault diagnosis method has become a hot topic. In order to solve the problem of optimal resonant band selection, noise reduction and frequency extraction in fault diagnosis, this paper presents a method of rolling bearing fault diagnosis based on self correlation function-cyclic spectrum analysis (SCF-CSA). Firstly the method selects the appropriate resonance band signal through the band pass filter based on the spectral kurtosis (SK). And then makes the self correlation analysis of the signal with the original vibration signal to reduce noise. Finally, the rolling bearing fault diagnosis is realized by cyclic spectrum analysis. It proved by experiment that this method not only overcomes the interference of noise caused by human factors and signal superimposed reconstruction to the diagnosis result, but also can be more targeted to analyze the slice energy of the fault feature frequency and realize the fault diagnosis of the rolling bearing.
Feature Extraction and Recognition of Fault Vibration Signals Based on KPCA and QPSO-BP.
Drive system is the central of rail vehicle and large machine, such as cardan shaft and gear box of the MU train. Identification of early vibration anomalies can effectively prevent further expansion of the fault. Taking the vibration signals of rolling bearing in strong noise background as an example, an automatic method of feature extraction and recognition based on Kernel-Based Principal Component Analysis (KPCA) and BP network optimized by Quantum-behaved Particle Swarm Optimization (QPSO-BP) is presented. Firstly 16 characteristics are calculated in timefrequency domain of denoised signals. After optimizing kernel function from Polynomial, Gaussian Radial Basis (GRB) and Multilayer Perceptron (MLP), the KPCA method is used to reduce the dimension of characteristics, and the eigenvector of different fault types is constructed as the input of BP network. From the view of quantum mechanics, the global search ability of traditional particle swarm optimization (PSO) is improved. In order to avoid falling into local minimum in network training and improve recognition accuracy, the initial weights and thresholds of BP network is optimized based on QPSO algorithm. The recognition accuracy of the traditional BP network is 87.1% while that of the improved QPSO-BP network model can reach 94.1%, which proves that this method can effectively diagnose the early fault of the drive system.
Investigation on Intelligent Recognition of Failure Axis Orbit Based on HWT and CNN
In condition monitoring and fault diagnosis systems of the large machine, such as the shafting of MU train or the rotating system of the compressor. Axis orbit can visually and comprehensively reflect the unit operating status. In this study, an intelligent recognition method of axis orbit based on Harmonic wavelet (HWT) and convolution neural network (CNN) is presented to deal with the problems of lower automation and accuracy. Firstly denoised axis orbit is obtained by reducing the noise of two-channel signals collected in the field using HWT with phase lock function. Then, Gaussian Blur is adopted to extend the training set for the small sample problem in deep learning. Automatic feature extraction and recognition are achieved by CNN network with LeNet-5 architecture. After optimizing the convolutional kernel parameters and training parameters, field data is used to verify the accuracy of the model which is higher than 99.96%, Meanwhile using the analog signal to produce the new training set with more complex and variable shapes, resulting in the accuracy of new model not less than 97.11%. This method has high practical value for the shafting running state detection and the early fault identification.
Analysis of Wheel-rail Vibration Signal Based on Frequency Sliced Wavelet Transform
In order to extract the features of wheel-rail vibration, a novel fault diagnosis approach using time-frequency slice analysis is proposed. Firstly, wheel-rail vibration signal was transformed by frequency sliced wavelet transform (FSWT) to get the time-frequency characteristic figures of wheel-rail impact. Secondly, the time-frequency region was selected on the basis of the characteristic of the signal energy distribution. Thirdly, the chosen time-frequency region is further refining analyzed to highlight the hidden time-frequency characteristics. Finally, the time-frequency region which contains fault feature is separated. Through inverse FSWT transformation, the signal component was reconstructed to separate the time-frequency characteristics of effective signals. The effectiveness of this approach has been proved through the dynamics simulation data, gives a new and efficient method to extracting the features of wheel-rail vibration.
Research on Stray Current Protection System for Light Railway Renewal and Reconstruction Project
In recent years, China's urban rail transit mileage has increased rapidly. Due to the current widespread use of DC traction power supply with running rail reflux, stray current will inevitably be generated in operation. With the extension of service time, the influence of stray current generated by DC power supply light railway transit system needs more and more attention. This includes electrochemical corrosion of steel bars in buried concrete pipelines, railway stations, section tunnels, viaducts and other concrete structures around the track. This paper expounds the significance of the automatic monitoring system for the light railway stray current corrosion installed since the opening of the Tianjin - Binhai Mass Transit, and explains the principle and the system composition of the self-developed stray current corrosion automatic monitoring system. It is proved that the measurement accuracy of the stray current corrosion automatic system meets the design requirements, and the stray current monitoring data of the key stations and new stations in recent years are also analyzed and discussed.
Vibration and Temperature Monitoring of Rolling Element Bearings in High Speed Trains
Rolling element bearings have been widely used in various mechanical facilities. It will cause great loss when bearing failure occurs. Many methods have been used to monitor the working status of bearings. A vibration and temperature monitoring system has been introduced to help monitoring the rolling element bearings in high speed trains. The characteristics of bearings failure have been analyzed and extracted in vibration and temperature signals. The bench test has proved the effectiveness of the monitoring method for gearbox bearings in high speed trains.
Feature Extraction and Recognition of Fault Vibration Signals Based on Principal Component Analysis and Neural Network: An Artificial Intelligence Approach
In high speed train maintenance, the damage detection and fault recognition for local defects at early stage is very important. Because, damages generated in drive systems like axle box bearing and gear box bearing have characteristics to grow fast and which can cause mass disaster. In this study, the authors report a fault diagnosis method by the combination of advanced signal processing technique with pattern recognition methods. Sixty fault vibration signals gathered under strong background noise are taken as a case study. On the basis of wavelet de-noising and singularity analysis, features of them in three domains are all computed for a time-frequencyenergy representation. Improved principal component analysis has been conducted in the three spaces for independent analysis and rational integration. A neural network recognition model with three binary numbers as the output mode has been established so that a series of tests on four typical or restructured eigenvectors is performed accordingly to find the optimal eigenvector for these vibration signals. Using this carefully designed feature extraction and recognition approach, not only would the fault vibration signals be detected as expected, but location and types of them could also be recognized. Thus feasibility of this artificial intelligence approach is implied for further research as well as industrial applications in rail transit and other related engineering fields.
Tread and Rail Fault Detection Algorithm Research Based on Space-time Continuous Vibration Sensor
Based on adding the vibration sensor on the rail which can be arranged continuously, the paper lays out a tread and rail fault detection algorithm based on continuous time and space. The algorithm compares the signal difference and the RMS value of each short period with the weighted RMS value of whole test signal to extract effective signal data, and then compare the effective data of each short period with calculated parameters to determine whether the device fault is present, and then make sure the fault occurs on the rail or on the tread by comparing more groups of data. At last, optical fiber sensor, as a vibration sensor which can be arranged continuously, is tested on the 1:5 multi-functional vehicle test circuit in Tongji University. The feasibility and effectiveness of the algorithm were initially verified.
The Study of Data Traceability of High-Speed EMU Based on PHM System
For the purpose of solving the emerging data quality problem of high-speed EMU in the process of train-to-ground data transmission, data processing and management, this paper studied the data traceability in combination with all kinds of data received and processed in PHM system. All of the data are sequentially tracked, analyzed and managed according to the process of receiving and analyzing, then visualization technology was used to display them in the PHM system and finally the research and application solutions of high-speed EMU data traceability based on PHM system were established. The implementation of the solution not only guarantees the integrity and accuracy of the application data in PHM but also further improves the transparency of the EMU operation information, so as to consolidate the data foundation for ensuring the safety of train operation.
Vibration Test and Data Analysis of Electric Locomotive Traction Motor on Running Railway Lines
Due to the failure of the bearing of Ying Shuiqiao running locomotive traction motor, there is a lot of failure. In order to investigate the influence of railway online on locomotive traction motor bearings, be tested vibration of locomotive traction motor bearing position. Compared with different railway lines, the bearing body is subjected to vibration impact to analysis the relationship between bearing failure with vibration energy online. In order to compare the traction motor energy levels of railway lines of different bearing failure rate, acceleration vs. time history of railway line of Ying Shuiqiao locomotive depot for high bearing failure rate and railway line of Hu Dong locomotive depot for failure-free are measured. Based on the power spectrum estimation data analysis method, the vibration magnitude of measurement points in two lines is summarized. The results show that the line with many faults is lower than that of the line with no fault. It shows that there is no direct connection between the fault of Ying Shuiqiao locomotive traction motor bearing failure with the vibration magnitude of railway line.
Gauge Measuring Method Based on 2D Laser Displacement Sensor
In order to eliminate the measurement errors caused by the rolling of the bogie during dynamic measurement of track irregularity, an algorithm for detecting the track irregularity based on the calibration of left and right 2D space coordinate system is proposed.The k-means method is used to segment the track data. Using least squares principle to extract the feature points of the curve and getting another feature point according to the track characteristics. Acquiring the relationship of the left and right sensor coordinate system. Acquiring the left and right track gauge feature points and unified to the same coordinate system. The gauge irregularity is obtained by the formula of distance between two points. Experimental results show that the measurement results have good repeatability. The result of manual check shows that the measurement error of gauge irregularity is less than 0.8mm, which meets the requirement of measurement.
Study on Evaluation Strategies and Criteria of Dynamic Performance of the Railway Freight Cars Applied to Condition Monitoring
The demand of rapid growth of the railway transportation equipment monitoring occurs with the rapid development of heavy-haul and rapid rail freight. The broad prospects for development are presented by the progress of testing, data processing and communication technology. Main effect of condition monitoring technology is the guarantee of railway wagon running safety and providing data support for the repairing time. The monitoring problems related with vehicle dynamic performance are researched only in this article. By means of the test and analysis of acceleration on the specific positions on the vehicle, the vehicle performance was graded and the results of all levels of performance form judgment are sent to the data center, and the vehicles subsequent handling is decided in accordance with the results above. It is discussed about the classification principles of vehicle dynamic performance, the possible cause of the change trend of vehicle dynamic performance and the coping strategies and measures. This is the support of the railway vehicle safety and repairing technology, has a broad space for development.
Research on Visualized Playback Function of the Train Operating Status Based on PHM System
This paper studies the visualized playback function of the train operating status for high speed EMU and applies it in the PHM system. It can provide the information support for operation staff to analyze the trace and parameter changes at a certain time. This study realizes the playback of the train operating status based on the train GPS data and operating status data and the visual effect is represented in the form of curve plotting with different kinds of plotting speeds. It combines the train operating parameters with the running track status to display the train running trace dynamically and the change of relevant state parameters, and hence guarantees the parameter analysis based on train operation track.
The Concept of the Time-Frequency-energy 3D Feature Library and Its Application on Structural Health Monitoring of the EMU Train
In this study, the concept of a time-frequency-energy 3D feature library has been introduced through advanced signal processing technology and pattern recognition methods. Sixty fault vibration signals generated by the axle box bearing of the EMU train are used for analysis. They are firstly pre-processed using discrete wavelet transform for both signal de-noising and singularity detection. Then feature extraction of the signature waveforms standardized by first-order differences and normalization has been performed from three perspectives: time, frequency and energy. Principal component analysis algorithm is then applied for dimensionality reduction in order to achieve integrated features which could better reflect vibration fluctuations in much smaller data sets. And finally the recognition model is established using selforganizing maps network with first principals obtained from the three domains as the input vector. It is found that using this artificial intelligence approach, excellent correlation between unidentified vibration fluctuations and point positions at a timefrequency- energy 3D feature space is achieved. And 100% accuracy rate of intelligent classification for the six vibration categories is realized with both reductions in data processing load and improvement in detection efficiency. The present work contributes to a new idea for the establishment of an intuitionistic feature library to realize real-time discrimination of all kinds of vibration signals generated by the EMU train. It may have a great prospect in the field of rail transit fault diagnosis
Real-time Fault Diagnosis of Rotating Machinery Based on 1-D Convolutional Neural Network
Reliability is one of the most significant aspects in evaluating rail vehicle system and rotating machinery accounts for most of the failures, which means the real-time condition monitoring of the rotating components is essential. As the key component of condition monitoring, diagnosis method has the problems of manual-needed fault feature extraction, low ability of complex fault recognition and high time-consuming, which unable to meet the real-time diagnosis demand of rotating machinery. This paper proposed a novel 1-D convolutional neural network (CNN) diagnosis model using modified 1-D convolutional kernel and pooling based on the classical CNN LeNet-5. Different from the typical intelligent diagnosis models taking feature extraction and classification as two distinct blocks, the proposed model fuses the two phases into a single learning body: adaptive feature learning by alternating convolutional and pooling layers while classification by full connected layer. Bearing and gearbox health monitoring results show that the proposed model can achieve fast and accurately fault diagnosis, which can satisfy the real-time diagnosis requirement of rail vehicles.
Operation of a Novel Temperature Compensation Module for Acousto-ultrasonic Wave Based Structural Health Monitoring on High Speed Trains
To ensure the safety of high speed trains during the entire lifetime of operation, an effective operational concept utilizing on-train/online SHM systems with existing scheduled maintenance procedures is crucially in demand. Online monitoring technologies have reshaped the traditional periodic maintenance procedures to condition-based maintenance procedures which have the benefits of enhancing the safety of trains and decreasing the cost of maintenance. The SHM solution for the high speed Electric Multiple Units (EMU) application performing real-time monitoring of the train bolster structure is called Active Train Online Monitoring System (ATOMS). The system comprises of the SMART Layers™ sensor network, Onboard ScanGenie data acquisition hardware, and a central computer with SHM Patch data analysis software installed. The SMART Layers™ are surface mounted at critical structural locations on the train using aerospace grade installation materials along with robust connectors to connect the SMART Layers™ with the onboard SHM hardware system.
Research on Fault Diagnosis of Traction Motor Based on Group Decision Making
In the fault diagnosis of traction motor, it is difficult to make a accurate and complete decision for the single fault diagnosis method. To handle this problem, a method of fault diagnosis for traction motor is proposed based on group decisionmaking in this paper. Firstly, the weights of the characteristic data are determined by intuitionistic fuzzy entropy, intuitionistic fuzzy cross entropy and foreground theory, according to traffic flow parameters the fault feature data. Secondly, fuzzy theory, neural network and support vector machine are used to diagnose the fault of the switch control circuit respectively. And then the three methods are considered as decision experts with the group decision theory. Through the simulation study, the prediction results of different diagnosis methods are given. The results show that the proposed method can accurately identify the fault type of the traction motor. This method is better than the single method to improve the accuracy of fault diagnosis, and has a good application prospect.
A Novel Machine Learning Technique for Online Health Monitoring of High-speed Trains
To ensure the operation safety and ride comfort of high-speed trains, a combination of smart sensory systems and intelligent identification models for online condition monitoring and assessment is highly desired. During routing operations, various dynamic responses induced by the wheel-rail interaction can cause severe wheel defects. The deterioration of train wheels, normally classified as “out-of-roundnessâ€ (OOR), can seriously threaten the operation safety and cause catastrophic derailment events. Conventional model-based prognostic methods often require an in-depth understanding of the wheel-track system to develop favorable mathematical models that are rather cumbersome. To complement the deficiencies of model-based prognostic approaches, the use of data-driven methods has been increasingly applied to various engineering fields. This research introduces a random forest (RF)-based method for online condition prediction and monitoring of train wheels. The RF-based method is a novel machine learning technique that possesses good stability and high accuracy for data classification with less parameter adjustment in modeling processes. A crucial step for the successful implementation of the RF-based technique is the data mining process to extract valuable feature information from the raw data. Therefore, the Teager-Kaiser energy operator (TKEO) and the wavelet packed decomposition (WPD) technique are integrated together for feature extraction in this work. The optimized feature subsets can thus be employed in the presented data-driven model for the online health monitoring of high-speed train wheels.
Vibration Characteristics of Direct-Fixation Track with Flexible Fasteners
Rail vibration were measured on railway of Taiwan Railway Administration to reveal the characteristics of direct-fixation track. Hammer test was first conducted to catch the natural frequency of direct fixation railway and frequency response function of the system. Train-induced vibrations were then measured to obtain the insertion loss of baseplates and the frequency spectrum of train loading. The post-vibration on the track was also collected and investigated to reveal effectiveness of fastener on vibration reduction. The results of field measurements were validated with the analysis of spring-mass model built with SIMPACK. It was concluded that 100 Hz and 630 Hz are the major frequencies of the track system. The fastener was found effective in vibration reduction induced by trains.
Sub-Nyquist Data Acquisition in High-Speed Rail Condition Monitoring by Means of Compressive Sensing
Monitoring data of axle box acceleration is critical because it can be used for detecting track defects and assessing the operation condition of a high-speed rail (HSR) system. However, there is a conflict between data volume and acquisition resolution: a high sampling rate leads to huge volumes of data while a low sampling rate cannot capture high-frequency vibration ingredients. Being an emerging theory, compressive sensing (CS) enables sub-Nyquist sampling when the target signal has a sparse representation in a certain domain. In this study, we take advantage of CS to deal with the above conflict. Using the axle box acceleration data acquired from a train running on a high-speed railway in China, we simulate the process of CS and investigate different ways of sparse representation. Likewise, we leverage the joint sparsity of successive signals to improve the reconstruction accuracy. It is found that the redundant dictionary outperforms the discrete concise transform (DCT) matrix and the discrete Fourier transform (DFT) matrix in sparse representation, leading to smaller reconstruction errors. More importantly, the feasibility of sub- Nyquist data acquisition in HSR condition monitoring is verified. In addition, joint reconstruction proves to be efficient for improving the reconstruction accuracy.
A Variational Bayesian Approach for Temporally Correlated Source Separation and Application to Thermal Strain Extraction of High-Speed Rail
Strain experienced by an in-service high-speed train is extremely important for ensuring the train’s structural safety because it gives a direct and unique insight into the real structural performance under various excitations. However, environmental factors, such as temperature, will significantly alter the monitoring data of strain and seriously affect the health assessment results. To eliminate the thermal effect on strain monitoring data, a hierarchical Bayesian approach in the context of Blind Source Separation (BSS) is explored in this study. Compared with the conventional independent component analysis (ICA) and second-order blind identification (SOBI) techniques, the Bayesian blind source separation approach can explicitly account for and quantify the measurement errors and uncertainty. More importantly, it directly gives rise to the (posterior) probability density distributions of the source signals, which greatly facilitate the reliability assessment of structural components making use of monitoring data. To accommodate non-i.i.d. temporal structure of the source signals, the Gaussian process kernel function is introduced in this study to define the prior distribution of the unknown sources. With this prior distribution in conjunction with appropriate priors for the mixing matrix and noise, the joint posterior distribution of the three groups of unknown parameters is derived by the Bayesian theorem. The Variational Bayesian (VB) learning is then applied to numerically obtain the probabilistic characteristics of the sources, mixing matrix, and noise separately. The real strain data acquired from a bogie in a high-speed train during its in-service operation is used to evaluate the performance of the proposed method through comparison with the ICA and SOBI techniques.
The In-transit Monitoring Method and Safety Assessment for Service Load of Lateral Steel-wood Stop at Long and Big Freight Cars
Aiming at the lateral dynamic load safety problem of large cargo railway transportation, a method for monitoring the lateral load of the load beam stop structure based on the lateral force flow transmission path is proposed. A combined lateral load transfer measuring device designed by cargo – wood stop - load sensing element – stop structure is designed. The device is used to monitor the lateral load of the real vehicle test in the well car transporting of large transformers, and the lateral transfer loads of different curves with different radius and different running speeds are analyzed. The conclusions are as follows: ( 1 ) During transportation ,the maximum transmission load of the lateral stop of the load beam is 66.1 kN , which occurs when the curve radius is 600m and the running speed is 46.99km/h , and the allowable load value which meets the safety requirements is lower than 141KN; (2) The transmission load of the lateral stop of the load beam has a linear relationship with the centrifugal force parameters of the train curve passing through, and the mathematical fitting expression of the transverse load and centrifugal force parameters is given, which provides a sub-important data reference for the static and dynamic fatigue safety assessment of the lateral stop structure before the overloading special transportation begins.
Real-time Safety Assurance of High-speed EMU Train Components via SHM Technology Based on PZT Sensor Network
Structural Health Monitoring (SHM) is an advanced technology which can inspect the integrity of structures in real-time and online by means of the integrated system of sensor, data collection device and algorithm. In order to accommodate the fast developed economic in China, the high-speed EMU train has attracted much attention during the past two decades. The combination of SHM technology and high speed EMU trains can not only extend the applying range of the SHM technology, but also enhance the real-time safety assurance of high-speed EMU trains. In this paper, a SHM technology based on PZT sensor network is explored to monitor the structural integrity of high-speed EMU train components. In order to justify the feasibility and reliability of the SHM technology under the operating conditions of high-speed EMU trains, several works were conducted: (1) Design of PZT sensor layout. (2) Wave propagation test; (3) Bench test; (4) Development of onboard SHM hardware and software; (5) Line test. Some essential achievements are obtained. Among these, in the bench test, fatigue cracks are found, positioned and evaluated, indicating that the SHM technology is sensitive for mini crack and is hopefully used to monitor defects in high-speed EMU train components. In the line test, the SHM system can operate smoothly and find a simulate defect, stating that the SHM technology is compatible with high speed EMU trains and can provide the real-time safety assurance..
Several Methods for Detecting the Number and the Interval of Defective Rolling Elements in Wheelset Bearing of High-speed Train
Two methods are proposed to detect the number and interval information of defective rolling elements in wheelset bearing of high-speed train. In the first method, the amplitudes of the fundamental train frequency and its harmonics are extracted from the envelope spectrum, then the normalized errors between the amplitudes and their theoretical values are calculated. Finally, the coordinate corresponding to the minimum error is the fault information. In the second method, an atom is constructed to execute a convolution with the squared envelope of the collected vibration signal; then the information is extracted from the inner product curves. Experiment verifies the effectiveness of the proposed methods.
Damage Fault Analysis for Subway Vehicle Fuse of Non-short Circuit
Non-short-circuit damage increases the economic cost of operating vehicle and affects the operational strategy of the operating circuit. In this paper, a set of fault diagnosis test system is designed based on NI as detecting hardware for metro vehicles with frequent non-short-circuit faults in the fuse. Dasylab software is used to build the system's acquisition part, and the overall statistical analysis of the test data is carried out of the train operation period. The VMD analysis is performed on the abnormal data that deviates from the mean value, and the frequent cause of non-short-circuit faults in the fuse is finally found. The results indicate that the fault diagnosis test system and analysis method designed in this paper are effective and reliable. The method has reliable engineering practical value.
Fault Diagnosis for Pantograph Based on Time-frequency Decomposition and Approximate Entropy
Railway transport is one of the most critical mass transportation media in the worldwide. With the development of trains speed, safety and comfort levels of railways are gradually and progressively becoming much more significant. Besides the high level of the security requirement, detection of anomaly for rail and road shall be early identified for decreasing operation and maintenance expenditures. The pantograph-catenary system has vital role in collecting the current in electrical railways. The problem occurred in this system will affect the current collection performance of electrified trains. In this paper, a fault feature extraction model of pantograph vibration signal based on time-frequency decomposition and approximate entropy (ApEn) is constructed. Firstly, ensemble empirical mode decomposition (EEMD) is conducted for vibration signal, then calculate ApEn after optimizing parameters for the intrinsic modal function obtained by EEMD. The ApEn features are input to the support vector machine (SVM) based on particle swarm optimization (PSO) to identify the pantograph fault identification. And the results show that the EEMD-ApEn fault diagnosis method has an excellent accuracy result for the vibration data of the pantograph panhead top pipe, yet is unsatisfactory for the vibration data of the carbon contact strip. According to this, the second-generation wavelet decomposition (SGWD)-ApEn method is used to improve the fault diagnosis results for the vibration data of the carbon contact strip. It verified the effectiveness of the combï€ ination with time-frequency decomposition and information entropy for pantograph fault diagnosis.
A Frequency-Weight Energy Operator and Empirical Wavelet Transform for Bearing Defect Detection
To achieve the early bearing fault diagnosis, this paper proposes a novel repetitive transients detection algorithm. The Fourier spectrum of the analyzed signal is firstly calculated. Then the scale-space plane (SSP) of the Fourier spectrum is obtained by scale-space representation (SSR). With the help of the SSP, the division of the Fourier spectrum is realized. According to the division, the signal is decomposed by empirical wavelet transform (EWT). In addition, the Hilbert transform (HT) is replaced by frequency weight energy operator (FWEO) to demodulate the filtered signal. The effectiveness of the proposed method has been certified by experiment signal. The comparison with the ensemble empirical mode decomposition (EEMD) is conducted to illustrate the superior of the proposed method.
An Improved Time and Frequency Analysis Method Combined with WVD and EEMD
In this paper, a time and frequency analysis method based on the Ensemble Empirical Mode Decomposition (EEMD) and Wigner-Ville distribution (WVD) is proposed. With the proposed method, time-varying vibration data are decomposed into a finite number of stationary Intrinsic Mode Functions (IMFs). In terms of the characteristics of impulse signal, the selection method of IMF based on power is studied. Wavelet Packets Decomposition (WPD), as a preprocessing procedure, is used to avoid the mode mixing. With the WVD, the time-frequency representation is shown. To verify the effectiveness of proposed method, we provide several case studies, from perspective of both simulation and online monitoring data collected from high speed inspection train. The results show that it performs obviously better than the existing time-frequency analysis methods.
A Smoothing Method for High-speed Railway Measured Rail Profile Data
The local distortion points of measured rail profiles have a great influence on the calculation results of wheel/rail contact parameters. To solve this problem, firstly, the measured rail profile is aligned with standard rail profile. Secondly, the normal distance between measured rail profile and standard rail profile was obtained and processed by using FFT, iFFT and boundary extension method. Finally, the smoothed normal distance was added to the standard rail profile to obtain the smoothed profile data. It is verified that the processed rail profile data is more conducive to wheel and rail contact parameters by comparing wheel/rail contact position, rolling radius difference (RRD) and equivalent conicity, before and after rail profile processing.
The Research on General AI-PaaS Architecture for Railway Application
With the development of AI technology, it is a trend to use algorithms to solve problems in the railway industry. This paper focuses on the architecture of the platform on which users can create their own AI application quickly and explains it in detail. With the support of two cases, this paper also comes up with the future development trends of AI-PaaS. As a conclusion, the AI-PaaS can improve the efficiency to build AI applications, and it will be more helpful for all the AI application developers.
New Detection Criteria and Shunting Monitoring in Railway Track CircuitReceivers
Track circuits play a major role in railway signaling. In some exceptional conditions, poor rail/wheel contact conditions may lead to a non-detection of the train on the zone. A presentation of the principle of detection by track circuits is proposed to introduce the existing detection criterion. The aim of the paper is first to present new detection approaches based on signal processing on an experiment with a dedicated train running on a track equipped with a track circuit. The second objective is to present a strategy to test new detection criteria on commercial zones over a long period of time (a few months) with the help of the PEGASE acquisition board. PEGASE has been developed by IFSTTAR and the presented work is the result of SNCF/IFSTTAR collaboration.
Degradation of Impact Attenuation of Fastening System in Mixed Traffic Trains in Thailand
Rapid expansion of railway networks in Thailand implies the necessity of effective maintenance and planning for the high reliability and sustainable railway operation. This requires insight in the infrastructure behavior during operation. The most common type of the railway networks in Thailand is ballasted track having high stiffness rail fastening system, i.e., elastic clips with stiff rail pad. Even though the fastening system is more likely suitable for medium axle load freight train rather than passenger trains, the system can be adapted to mixed traffic for efficient train operation. In the present work, the influence of repeated load on the deterioration of rail pad properties, e.g., stiffness and its impact attenuation ability, was investigated by the dynamic stiffness test (EN 13146-9) and impact attenuation test (EN13146- 3). The findings could be served as the foundation for the appropriate maintenance measure based on the operational usage of mixed passenger and freight train for Thailand’s railway infrastructure.
Fault Diagnosis Method Based on Transfer Learning and Deep ConvNet
With the development of Internet of Things (IoT) and Cyber-Physical System (CPS), a massive amount of real-time condition monitoring data of equipment are collected. Thus data-driven fault diagnosis methods have become more and more important in modern industry. However, traditional methods are based on handcrafted features which rely on the expert knowledge and experience. In addition, the distribution of training dataset is different from the distribution of testing dataset in many real-world fault diagnosis problems, which has not been taken into consideration in most existing data-driven methods. In this paper, we propose a novel fault diagnosis approach based on transfer learning (TL) and deep convolutional network (ConvNet). A convolutional neural network (CNN) structure is established to learn the representative features automatically from the converted images with abundant fault information and classify fault types. Furthermore, the performance for fault diagnosis on the target domain is improved by reusing parameters trained on the different but related distribution dataset on the source domain. The proposed approach is validated on the famous bearing dataset, constructed by the Case Western Reserve University (CWRU). The experimental results indicate that the proposed approach confirms faster convergence rate and higher accuracy compared to fault diagnosis based on deep traditional learning methods.
Study on Optimal Allocation of Reliability of High-speed Train Gear Transmission System Based on Improved Cost Function
Since the components of the high-speed train gear transmission system (HTGTS) are manufactured according to the requirements of the design, it is of great significance to optimize the reliability of each component in the reliability design stage to improve the service safety of the high-speed train. Currently many researchers focus on the reliability requirement, however, ignore the cost constraints in the improvement of component reliability. The Agree allocation method and the optimal allocation method based on cost function are discussed in this paper. Combining the characteristics of the two methods, an improved method for optimal allocation of cost function is proposed, and examples of the three methods are compared on HTGTS. The results show that the proposed method has significant effect on improving the reliability of the HTGTS and provides theoretical support for enhancing the reliability design of high-speed train components.
Oil Ring of EMU Gearbox Press-in Position Detection Method Based on Press-fit Curve
CRH(China Railways High-speed) has been experiencing flourishing development in recent years, and the quality of high-speed EMU (Electric Multiple Units) not only has an impact on China Railways operation security, but also concerns the international execution of the Belt and Road Initiative. Gearbox, as the key component of EMU bogie, bears high load and severe vibration, and due to its sophisticated structure, large amount of components and high precision of assembly, the maintenance of EMU gearbox is of substantial significance in the overhaul of the whole EMU. However, in the maintenance of EMU gearbox, traditionally it is impossible to make sure that the oil ring is in place after being press-fitted, since the view has been blocked by the bearing cage. Therefore, a series of technical research has been carried out to study properties of the pressure-fit curve of the press machine, as well as reveal the principle indicated by it. Eventually, an approach based on the press-fit curve to determining whether the oil ring is in place has been devised.
Research on Electromagnetic Relay Health Status and Life Evaluation Technology of CRH3 Series EMUs
This paper summarizes the relay types, working principle fault distribution principle of CRH3 series EMUs. It introduces the two commonly used analysis and evaluation methods and compares them. The three types of relays which are not installed and used ,have operated 1.2 million kilometers and faults were selected. The variation of the parameters such as pull-in voltage, release voltage, and contact resistance with temperature was analyzed. The Fick diffusion equation was used to build Larson. - Miller model, at the same time, analysis and excavation of big data for test data, and obtained the change principle of relay parameters with temperature and mileage. The results show that the analysis method and test analysis results can be used for the health status of CRH3 series EMU relays. And evaluate the remaining life.
The Intelligent Diagnosis Research of TCMS Based on a Port Condition Analysis Method
The communication quality of on-board network system influences the transmission of control commands, status data and fault message, which plays an significant role in the high-speed train's reliable operation. Considering the complexity of operating environment, the train control and monitor system (TCMS) is often affected by high voltage fluctuations, the vehicle electromagnetic environment, cable layout and component failures, making it difficult to troubleshoot and locate faults. Given multi-functional vehicle bus (MVB) communication mechanism and the train network topology, this paper proposes a health evaluation model based on the analysis of network port condition, which may make a contribute to the predictive maintenance and troubleshooting of the TCMS. This model is built on the evaluation of the waveform distortion and the error rate of port periodic frames. By real-time and historical data analysis, the current condition of TCMS can be diagnosed and the fault can be predicted. Finally, our work is verified by experiments on the ground, convincing the highly feasibility of the proposed model.
Framework of Big Data Monitoring System for Locomotive-network’s Electrical Interaction and Its Data Mining Application
Electric locomotive (EL) and EMUs acquire current from traction power supply system (TPSS) through the pantograph on board. The process of acquiring the current from TPSS contain abundant electrical transient and steady state phenomena which can reflect the TPSS, EL, and EMUs’ health status. For example, the parameter identification, the fault early warning and the electrical coupling performance between TPSS and EMUs could be calculated and evaluated with data mining method from the current and voltage. This paper proposed the framework of big data monitoring system for electrical interaction of locomotive-network (LN). And based on the electrical data obtained from the monitoring system, the application cases of harmonic resonance frequency identification, harmonic impedance calculation and spectrum’s time-varying characteristic of electric locomotive in TPSS were presented in this paper.
Analysis and Majorization of Rail Transit Vehicle IGBT Device Lifetime Prediction Algorithm
Insulated Gate Bipolar Transistor (IGBT), as the key device of rail transit vehicle, plays an important role in Health Monitoring of Rail Transit Structure. But how to make an accurate prediction to the device lifetime is still a technical problem. Over the years, researchers have proposed many algorithms, such as ABB’ look-up table method [1], Infineon’ cycle curve factor correction method etc [2]. Based on operation curve simulated by semi-physical simulation platform and MATLAB with Plecs joint simulation platform, this paper proposes a lifetime prediction algorithm comparing the lifetime prediction algorithms of Mitsubishi, Infineon and ABB. During the calculation of lifetime prediction, rainflow algorithm is optimized for extract the temperature load changes and quadratic fitting method is utilized to obtain life prediction model parameters.
Rolling Bearing Fault Diagnosis Method Based on Cepstrum Pre-whitening and Hilbert Envelope Demodulation
The research and application of on-line detection and fault diagnosis of high-speed railway bearing can effectively ensure the high-speed railway operation safety and save the maintenance cost. In order to extract fault feature of bearing (axle box bearing was studied) efficiently, and eliminate discrete frequencies caused by shock and strong background noise, a new method that based on combination of pre-whitening technology using cepstrum editing procedure (CEP) and Hilbert envelop demodulation was presented. Signal pre-whitening could enhance the impulsiveness of the bearing fault, and isolate the periodic frequencies in complex vibration signal. The prewhitening signal only contained non-stationary impact components and background noise. The frequency spectrum analysis should apply to the signal after Hilbert envelop demodulation, which did not have strong noise interference and could extract the fault feature. The result shows that this method can efficiently extract the fault feature of defective rolling bearing faults.
Earth Fault Analysis and Principle Mining of Traction System of CRH3 Series Multiple Units
This paper describes the damage degree of the earth fault of the traction system and the reasons of the earth fault. The analysis and statistics of the earth faults are carried out according to the time and environment dimensions of Jing-Hu and Wu- Guang lines. The types and theory of earth faults in the traction system are introduced. At the same time, the data acquisition system of the vehicle-mounted IPC(Industrial Personal Computer) is developed. The 25EF earth fault data is collected in real time. The mathematical data analysis such as Bayesian is used to filter and mine the big data, and the development pricinple and trend of the earth fault of the traction system has obtained. This method has a guiding role in predicting the traction earth fault of the CRH3 Series EMU.
Research on Predictive Maintenance of Traction Converter Filter Based on PHM System
Based on the existing PHM system, the predictive maintenance research for the traction converter filter of the standard EMU train is studied. This paper studied the influence of filter plugging on the performance of cooling system, and established a predictive model for traction converter filter maintenance through big data analysis and machine learning methods. As a result, it can guide the filter maintenance, reduce the cost of maintenance and the workload of personnel, ensure the safety of the train operation, and make EMU operations shift from traditional planned maintenance to predictive maintenance based on condition.
IGBT Device Lifetime Prediction of Rail Transit Based on Semi-physical Simulation
Insulated Gate Bipolar Transistor (IGBT) used in rail transit work in complex environments such as high voltage/high current/high frequency switching status for a long time. Frequent fluctuations in power consumption and junction temperature of IGBTs can cause fatigue aging of the devices. Therefore, the life prediction technology of IGBT devices is crucial. Therefore, IGBT device lifetime prediction plays an important role in Railway System Health Monitoring. In this paper, a semiphysical simulation platform is introduced to simulate locomotive operation circumstance, then IGBT junction temperature can be calculated by MATLAB according to mission profile. Ultimately, IGBT device lifetime prediction is completed through rain flow algorithm and life model.
The Design Exploration of PHM System of Trailer in New-type EMU with Centralized Power
This article analyzes the technical status and existing problems for Train Coach Running Detection System(TCDS）for 25G/T-type passenger trains in existing railway trunk lines and put forwards the concept that builds an on-board PHM system on trailer in EMUs with centralized power based on train-level Ethernet technology and vehicle-ground wireless transmission technology so as to realize the functions such as state-aware, state monitoring and data transmission for on-board key equipment; meanwhile, realize the interconnectivity of safety monitoring information between coaches and locomotives through fault and state classification diagnosis for sub-system components in locomotives and coaches; realize the state assessment, fault prediction and health management of train’s components in a triune manner as “running train – operator - manufacturerâ€ through two-way wireless information transmission with ground PHM system.
Study on the Fatigue Life of Aluminum Honeycomb Floor for High Speed Train
The forcing magnitude and variation of high-speed train's aluminum honeycomb floor under actual operating conditions determine the fatigue life of the floor. In this paper, based on the physical properties of aluminum honeycomb floor in different lifetime and different support span, the loading characteristics and physical performance evolution characteristics were tested and analyzed for high-speed train's aluminum honeycomb floor; And combined with the vibration characteristic under actual operating conditions of high-speed train and the real loading model of the aluminum honeycomb floor, the finite element simulation was used to analyze the dynamic stress of the floor structure under dynamic load in order to provide load conditions for the standard sample fatigue test; Finally, the fatigue performance of the floor structure in different loading level was tested and analyzed, the fatigue life curve of the common load ratio of the honeycomb sandwich structure was obtained to guide the overhaul and design of the high-speed train’s floor structure.
The Regulation of Maintenance Period Based on Damage-tolerance Theory
The concept of damage-tolerance has been widely used in the design and maintenance of aircraft; however, for existing railway vehicle structural parts, the establishment of the maintenance plan period is often based on experience. Scientific and reasonable maintenance cycle can not only guarantee the safety of train operation, but also can save the manpower and material resources due to too frequent maintenance. Based on bolster as an example, this paper introduce how the damagetolerance based theory implemented to establish maintenance regulation in the application, in terms of the nondestructive testing, structural failure judgment simulation based on Paris formula. Through the damage-tolerance method, it is very important to realize the health management of the structural parts of the railway vehicles, and furthermore to realize the whole life cycle health management of the train.
Health Management System of the Locomotive Complex Cooling Tower
Basic constitution of the health management system has been introduced. Fundamental models of venting surface, fan motor bearing and water status management method have been proposed, and the health management system developed based on these models have been applied to some locomotives
IGBT Device Lifetime Prediction Technology of Rail Transit Based on MATLAB and PLECS
Lifetime prediction of IGBT device has been a hot topic between device manufacturers and users. A new method of lifetime prediction is proposed based on MATLAB and PLECS. Electro-thermal modules of IGBT and control circuit of system are built. Power loss, junction temperature and case temperature can be extracted. The temperature swings can be counted by rain flow algorithm, and then lifetime prediction of IGBT device can be completed with lifetime model. The proposed method has the feature of good applicability, which is suited for lifetime prediction of IGBT in different converter without knowing real-time data.
Train Bearing Temperature Trediction Based on LSTM Recurrent Neural Network
The bearing temperature is an important indicator of the bearing state during the operation of the EMU train, and it is also an important physical parameter for the performance evaluation of bearing during long-term service. Due to factors such as train operating conditions, ambient factors, and thermal accumulation effects, it is difficult to obtain good results only by setting the bearing temperature threshold to evaluate the bearing operating state. Based on the big data application development platform, based on the analysis of a large number of operation data of bearing temperature, the LSTM recurrent neural network is used to establish the bearing temperature prediction model, and the EMU bearing operation data is used to train and test the model to obtain the full operating condition and the full climate environment. The model is verified by the actual operating datum of a certain EMU train. The comparison with the operational test data shows the correctness and effectiveness of the proposed method.
Analysis of Pantograph Failure Prediction for CRH3 EMUs
The faults form the pantograph are key factors thar can affect the efficiency of the railway transportation and causing safety accidents. Therefore, accurate diagnosis and prediction of pantograph faults are of great significance to improving traffic safety. There is a strong correlation between the flow conditions of the EMUs during operation and the bow-net contact pressure. The contact pressure of the pantograph and the catenary can intuitively reflect the contact state between the pantograph slide and the catenary. According to the measurement data of the pantograph contact pressure and the thickness of the slide, and the real-time network pressure data, combined with the key fault types, the bow can be extracted. The fault eigenvalues of the network are used to construct a pantograph fault prediction model and a pantograph fault prediction analysis is performed.
Risk-informed Real Time Decision Making of Fatigue-sensitive Structures within High-speed Train
Corrosion and fatigue can affect structural performance and ultimately can cause structural failure. For instance, generally the train bogie components experience many stress cycles during routine high-speed train journeys, which could result in cumulative fatigue damage and eventually leading to fatigue failure. Thus, monitoring and inspection events are needed to ensure structural safety. Inspection events, such as non-destructive inspections, can be used to estimate the performance of high-speed train details that are prone to fatigue and corrosion. Based on the inspection and monitoring results, performance of the inspected and uninspected details with railway systems can be updated and planned. Overall, the probabilistic fatigue performance assessment and optimal planning using inspection events will be conducted in this paper to ensure structural safety and functionality during the service life of high-speed trains.
Condition-based Maintenance of High-speed Train Wheels Through Trackside Monitoring
Condition-based maintenance (CBM) is a promising strategy in reducing the operating cost for modern high-speed rail (HSR), especially for train wheelsets. This paper presents a research effort on wheel defect detection in high-speed trains by devising a fiber Bragg grating (FBG)-based online monitoring system deployed on rail tracks to detect both wheel local defect and wheel polygonization during the passage of in-service high-speed trains. A diagnostic algorithm is developed using the online monitoring rail response to automatically identify both potential wheel local defects and polygonal wears. The devised monitoring system is then deployed on a test track to experimentally verify its capability to detect wheel defects. Two blind tests are conducted by running a train with potential wheel local defects and wheel polygonization on the instrumented rail, respectively. Results indicate that both local defects and polygonal wears can be identified with high fidelity, with the identified results being in good agreement with the results of radius deviation measurement later conducted offline in a depot.
Online Wheel Condition Detection Using Standard and Heteroscedastic Gaussian Process Learnings
The vibration-based wheel condition monitoring may suffer from measurement noises as well as stochastic wheel/rail dynamics; in this regard, statistical methods are believed to be more promising in dealing with these variabilities in monitoring data. This paper proposes two state-of-the-art Bayesian machine learning approaches for online wheel condition assessment based on dynamic strain monitoring of rail tracks. In this first approach, wheel quality-sensitive ingredients are firstly extracted from the original signals and their Fourier amplitude spectra are normalized to characterize informative features for assessment of wheel qualities. A statistical model is then formulated by standard Gaussian process regression for representation of wheels under normal condition such that wheel conditions can be quantitatively and qualitatively evaluated with the aid of Bayes factor once new monitoring data are available. In the second approach, the uncorrelated and uniform assumption on measurement noises in standard Gaussian process is removed and heteroscedastic Gaussian process allows error variance to vary over time, resulting in more robust and reliable diagnosis of wheel qualities when heteroscedasticity is manifested in the data. The two proposed approaches are examined by using online strain monitoring data collected by an optic fiber sensory system.
Research on Fault Diagnosis Algorithm for the Gearbox and Coupling of EMU Based on Neural Network
The gearbox and coupling are key components for EMU to transmit torque and drive vehicles moving. Its faults will directly impact the EMU operating reliability and even endanger the driving safety. Taking into account four categories of fault pattern for single-axle gearbox and coupling and twenty kinds of state for a train, train-level fault identification algorithm for the gearbox and coupling and component-level fault location algorithm for axle are designed using neural network model in order to realize the fault diagnosis for the gearbox and coupling of EMU. Forty-five eigenvalues are selected as the input data through correlation analysis including motor voltage, motor current, rotational speed, temperature, vibration signal and accumulated running time. Furthermore, single fault tests and comprehensive fault tests are conducted to verify the generalization ability of the algorithm using rolling vibration test rig. The results demonstrate that the proposed fault diagnosis algorithm can effectively identity the corresponding fault.
Bayesian Approach for Fatigue Life Assessment of High-speed Trains Using In-service Monitoring Data
This paper presents a method for fatigue life prediction of high-speed trains using in-service monitoring data, which establishes a Bayesian framework to take into account the uncertainties of the material parameters and in-service stresses imposed on the train. In this regard, a statistical treatment of fatigue test data and in-service stresses of the high-speed train components is required, which leads to probabilistic formulations of both the S-N relationship and stress spectrum. The formulated models allow the description of uncertainties in both the material parameters and in-service stress ranges from the posterior probability density functions (PDFs) updated by Bayesian inference. Subsequently, the information of these PDFs is utilized to develop a fully Bayesian procedure for predicting fatigue life of the high-speed train components. For verification, the proposed method is applied to predict the fatigue life of a train running on a China high-speed railway. Analytical solution for model derivations is provided. The probabilistic variance of the evaluated results of fatigue life in the Bayesian context demonstrates the influence of uncertainties inherent in the material properties and measurements.
Hybrid Prognostics Model for Life Cycle Management of High Speed Train
As the volumes of service of subways and high-speed Electric Multiple Units (EMU) trains have significantly grown in recent years, the life cycle maintenance operation plan for these newly delivered rail vehicles becomes critical. Emerging structural health monitoring (SHM) technologies are introduced to revolutionize the operational procedures for the maintenance of these rail vehicles. To ensure the train safety during the whole lifetime of operation, an effective operation concept to integrate the on-train/online SHM systems with the existing scheduled maintenance procedures is crucially in demand. High-speed EMU trains are obligated with their maintenance schedules before they are delivered to passenger transportation services. The vision of a complete train lifecycle management model will involve multiple tiers in data collection, data distribution and information dissemination, data networking, big data storage and processing, and operational decision management and control
Intelligent Health State Awareness Technology Framework for Maintenance-free Railway System Vision
Aiming at reducing the train accidents and enabling the railway system maintenance-free vision, this paper outlines an artificial intelligence (AI)-machine learning-based monitoring technology framework, which is capable of autonomously providing the health state awareness of the wheel-track system and direct feedbacks for adaptive positive train control (PTC).
A Sparse Array Structural Health Monitoring System Based on SH Wave
Structural health monitoring (SHM) is becoming more and more important in modern industries. The fundamental shear horizontal (SH0) wave based inspection is very attractive since SH0 wave is the unique non-dispersive wave mode in plate-like structures. For practical SHM applications, a sparse array system based on omni-directional transducers is more promising due to the low cost and simple signal processing. In this paper, we firstly proposed a new omni-directional SH wave piezoelectric transducer (OSH-PT) consisting of twelve thickness-poled, thickness-shear PZT elements. Experimental results indicated it can generate and receive single mode SH0 wave in wideband with uniform sensitivity along all directions. Then, the number of elements composing the OSH-PT was decreased to two to improve its practicability. Its performance was investigated to be acceptable. After that, the existing four types of OSH-PTs were comprehensive compared and discussed. Finally, experiments were carried out to examine the feasibility of OSH-PT based SHM system. Results indicated the proposed SHM system can accurately locate the defect with the super resolution of 0.12λ (4mm) at varied frequencies. Besides, it can also locate multi defects based on the baseline subtraction method. The good performance of the proposed SHM system was benefited from the employed single mode non-dispersive SH0 wave. This work is expected to promote the applications of SH0 wave-based SHM.
A train draft gear goes between the coupler and the frame of the car. The train draft gear cushions switching impacts so that the impacts don't damage the train cars. Currently, the inspection of train draft gears is mainly performed offline through visual inspection. Because the major part of the draft gear is hidden inside the train coupler, it requires the disassembly of train coupler to perform the inspection, which is time consuming. Moreover, when there is a leakage inside the draft gear, visual inspection could miss the internal leakage until the leakage is very severe. This paper introduces a Structural Health Monitoring (SHM) system to monitor the status of the train draft gear when the train is in service. The system consists of a set of strain sensors to measure the pulling and pushing force, a data acquisition unit and the upper level analysis software. When the draft gear is mal-functioning, strain signals show different patterns from the signals in normal condition, which can be used by machine learning methods to estimate the health status of the draft gear.
Research How to Monitor Wagon Brake Health Online
The writer is going to introduce a kind of health monitoring system that monitors railway wagon Air brake system. Air brake system works in its intrinsic rule. The monitoring system detects the air pressure of key dot of air brake system, analyzes the variable pressure data, diagnoses the brake faults, makes sure the health of air brake system of wagon. In order to look for health condition of air brake in any time on line, the monitoring system sends the data to data center though wireless ad hoc networks. This monitoring system needn't alter the structure of wagon, change the arrangement of air brake system, inflect the management of operation.
Three-dimensional Temperature Field Simulation Analysis of Asynchronous Traction Motor
Temperature Rise in electric motor is an important factor, which affects its reliability and life. In order to accurately understand the temperature filed in the traction motor. this paper takes an asynchronous traction motor by forced air circulation for research object, and analyses the three-dimensional temperature field. In this paper, finite element analysis 3D model of an asynchronous traction motor has been established, which also includes the fluid domain. Using the finite element method for the three-dimensional temperature field in different ventilation structure and various load conditions. This study provides the temperature rise changing in flow rate, which can reference for motor design and optimize the performance.
Validation of a Bingham Snow Model by the MPS Method
This paper aims to validate a MPS Bingham snow model for train safety driving analysis. Fluidized snow is assumed as a Bingham fluid in this study, for which the shear stress is obtained according to the Mohr-Coulomb failure criterion and the viscosity terms in the Navier-Stokes equations are discretized using the MPS gradient model. The constitutive Bingham snow model is validated through comparison of numerical simulations with a laboratory experiment in references, good agreement of the impact force has been obtained. The applicability of this MPS Bingham snow model is confirmed, however, validations are still required to make it convincing.
Research and Application of Indicator-based Rule Engine for WTD Data of High Speed EMU
For the complexity of high-speed EMU operation data，it is difficult for business personnel to deal with the deficiency of monitoring data in real-time. This paper constructs the rule engine for the WTD data of high-speed EMU based on indicator system, then combined and applied in PHM System effectively. First of all, the classification indicator system of vehicle WTD data is formed through indicator construction, which is illustrated through a tree diagram in the PHM system. Secondly, a three-layer data storage structure is established according to the indicator system, which consists of the online monitoring layer, data integration layer and online / offline mining layer. And then on top of what has stated above, the business personnel can form the expression through logical value configuration and simulated value configuration and finally form an online mechanism rule model through real time streaming calculation. This research and application will reduce the dependence on the program development for the business personnel. It can also increase the efficiency of the development for the mechanism rule model so as to ensure the safety of train operations.
Train Bogie Damage Detection: Review and Prospect
The bogie is an important unit for carrying vehicle body. Therefore, it is important to detect the damage and defects inside the bogie structure in time to ensure the safety of train operation. This paper gives a comprehensive overview of the basic methods and current situation of bogie damage detection. On the basis of summarizing the previous research, a new detection method for bogie damage defects is proposed to change the traditional passive detection method. The technology is easy in operation, rapid in detection, and meets the needs of monitoring the damage of the bogie in transit. Compared with traditional detection methods, this technology can detect micro internal damage in time and has good economic and social benefits.
Three-dimensional Crack Propagation Simulation Experiment Comparison and Life Prediction Based on Paris Formula
Based on the theory of linear elastic fracture mechanics, A three-dimensional crack propagation simulation model of a standard compact tensile specimen was established. The crack front stress intensity factor was calculated. The crack front stress intensity factor was calculated and the relationship between it and crack length was calculated. The critical crack length was determined. The initial crack was automatically expanded and the crack propagation path was obtained. Based on the Paris formula, the fatigue crack growth model of the test is determined and the crack propagation life prediction of the sample is completed. Finally, by comparison with the test results, it is proved that the crack propagation life prediction based on this method is reasonable and effective.
Research on Condition Monitoring of Bearing Based on Factor Analysis and Logistic Regression
Along with the introduction of the concept of industrialization 4.0, we are taking another step towards the times of big data. The informationized, datamation and intelligent development of industry pushes the development of monitoring on rail traffic health, which makes accurate and effective monitoring on trains status and prevention of severe accidents become an inexorable trend. The research object of the paper is rolling bearing which is one of the key components of pulling motor. The research process it that collect the vibration signals of bearing through acceleration sensor, pretreat the raw data, then extract the feature indexes, carry out factor analysis on feature indexes and research into the correlation among feature indexes, surmount the subjectivity for index selection, then realize dimensionality reduction of big data, establish the bearing status deterioration assessment model with logic regression and get the health degree through calculation of real-time feature indexes, then monitor the operation state of bearing according the health degree , and find problem timely, thus reduce the accident incidence and maintenance cost and raise economic benefit.
Rail Transit Prognostic and Health Management Based on Big Data
In this paper, a rail transit prognostic and health management method based on big data was investigated to achieve the requirements on cost-saving, profit-increasing and lean R&D in the rail transit industry. Firstly, the necessity of applying big data to the prognostic and health management was analyzed. Then, the technical framework of rail transit prognostic and health management platform (RPHMP) based on big data was proposed. Additionally, the application associativity of data mining algorithms in RPHMP was analyzed. On this basis, the cloud computing's role of application support to RPHMP on the three layers of infrastructure, platform and software was summarized. Finally, the application flow of the RPHMP based on big data was depicted.
A Realistic Model on the Wheel-track Contact: An Infinite Beam on a Bilinear Elastic Foundation Under a Concentrated Load
The response of an infinite beam on an elastic foundation depends on the property/modeling of the foundation. There is a qualitative difference between the responses of a beam when it is on the Winkler foundation and on the tensionless foundation. A bilinear elastic foundation model, which describes the different behaviors of the elastic foundation in the tensile and compressive zones with two different foundation moduli, is proposed and a straightforward computational method is also formulated. The Winkler and tensionless foundations are shown to be the two special cases of the bilinear elastic foundation model. With this bilinear elastic foundation model, a more general method of modeling an elastic foundation is provided, which can be of some help to the modeling of the support of the ballastless high-speed railway.