A Tutorial on Hardware-Implemented Fault Injection and Online Fault Diagnosis for High-Speed Trains

Electrical drive systems are the core of high-speed trains, providing energy transmission from electric power to traction force. Therefore, their safety and reliability topics are always active in practice. Among the current research, fault injection (FI) and fault diagnosis (FD) are representative techniques, where FI is an important way to recur faults, and FD ensures the recurring faults can be successfully detected as soon as possible. In this paper, a tutorial on a hardware-implemented (HIL) platform that blends FI and FD techniques is given for electrical drive systems of high-speed trains. The main contributions of this work are fourfold: (1) An HIL platform is elaborated for realistic simulation of faults, which provides the test and verification environment for FD tasks. (2) Basics of both the static and dynamic FD methods are reviewed, whose purpose is to guide the engineers and researchers. (3) Multiple performance indexes are defined for comprehensively evaluating the FD approaches from the application viewpoints. (4) It is an integrated platform making the FI and FD work together. Finally, a summary of FD research based on the HIL platform is made.

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Sensors Information Fusion System with Fault Detection Based on Multi-Manifold Regularization Neighborhood Preserving Embedding

Sensors ◽

10.3390/s19061440 ◽

2019 ◽

Vol 19 (6) ◽

pp. 1440

Author(s):

Jianping Wu ◽

Bin Jiang ◽

Hongtian Chen ◽

Jianwei Liu

Keyword(s):

Fault Detection ◽

Information Fusion ◽

High Speed ◽

Euclidean Distance ◽

Manifold Regularization ◽

Fusion System ◽

Electrical Drive ◽

High Speed Trains ◽

Sensor Information ◽

Drive Systems

Electrical drive systems play an increasingly important role in high-speed trains. The whole system is equipped with sensors that support complicated information fusion, which means the performance around this system ought to be monitored especially during incipient changes. In such situation, it is crucial to distinguish faulty state from observed normal state because of the dire consequences closed-loop faults might bring. In this research, an optimal neighborhood preserving embedding (NPE) method called multi-manifold regularization NPE (MMRNPE) is proposed to detect various faults in an electrical drive sensor information fusion system. By taking locality preserving embedding into account, the proposed methodology extends the united application of Euclidean distance of both designated points and paired points, which guarantees the access to both local and global sensor information. Meanwhile, this structure fuses several manifolds to extract their own features. In addition, parameters are allocated in diverse manifolds to seek an optimal combination of manifolds while entropy of information with parameters is also selected to avoid the overweight of single manifold. Moreover, an experimental test based on the platform was built to validate the MMRNPE approach and demonstrate the effectiveness of the fault detection. Results and observations show that the proposed MMRNPE offers a better fault detection representation in comparison with NPE.

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A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches

Entropy ◽

10.3390/e23010001 ◽

2020 ◽

Vol 23 (1) ◽

pp. 1

Author(s):

Chao Cheng ◽

Jiuhe Wang ◽

Hongtian Chen ◽

Zhiwen Chen ◽

Hao Luo ◽

...

Keyword(s):

Fault Diagnosis ◽

High Speed ◽

System Structure ◽

Future Research ◽

Major Focus ◽

Types Of Knowledge ◽

Qualitative Approaches ◽

High Speed Trains ◽

Safety And Reliability ◽

Important Branch

For ensuring the safety and reliability of high-speed trains, fault diagnosis (FD) technique plays an important role. Benefiting from the rapid developments of artificial intelligence, intelligent FD (IFD) strategies have obtained much attention in the field of academics and applications, where the qualitative approach is an important branch. Therefore, this survey will present a comprehensive review of these qualitative approaches from both theoretical and practical aspects. The primary task of this paper is to review the current development of these qualitative IFD techniques and then to present some of the latest results. Another major focus of our research is to introduce the background of high-speed trains, like the composition of the core subsystems, system structure, etc., based on which it becomes convenient for researchers to extract the diagnostic knowledge of high-speed trains, where the purpose is to understand how to use these types of knowledge. By reasonable utilization of the knowledge, it is hopeful to address various challenges caused by the coupling among subsystems of high-speed trains. Furthermore, future research trends for qualitative IFD approaches are also presented.

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Fault Diagnosis Based on 1D-CNN with Associated Auxiliary Tasks Boosted for Wheelset Bearings of High-Speed Trains

2020 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) ◽

10.1109/sdpc49476.2020.9353116 ◽

2020 ◽

Author(s):

Junjie Liu ◽

Huan Wang ◽

Zhiliang Liu ◽

Zilin Wang

Keyword(s):

Fault Diagnosis ◽

High Speed ◽

High Speed Trains

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Comparative study on the use of acoustic emission and vibration analyses for the bearing fault diagnosis of high-speed trains

Structural Health Monitoring ◽

10.1177/14759217211036025 ◽

2021 ◽

pp. 147592172110360

Author(s):

Dongming Hou ◽

Hongyuan Qi ◽

Honglin Luo ◽

Cuiping Wang ◽

Jiangtian Yang

Keyword(s):

Acoustic Emission ◽

Fault Diagnosis ◽

High Speed ◽

High Speed Train ◽

Signal Attenuation ◽

Transmission Path ◽

Bearing Fault Diagnosis ◽

Operation Conditions ◽

Fault Features ◽

High Speed Trains

A wheel set bearing is an important supporting component of a high-speed train. Its quality and performance directly determine the overall safety of the train. Therefore, monitoring a wheel set bearing’s conditions for an early fault diagnosis is vital to ensure the safe operation of high-speed trains. However, the collected signals are often contaminated by environmental noise, transmission path, and signal attenuation because of the complexity of high-speed train systems and poor operation conditions, making it difficult to extract the early fault features of the wheel set bearing accurately. Vibration monitoring is most widely used for bearing fault diagnosis, with the acoustic emission (AE) technology emerging as a powerful tool. This article reports a comparison between vibration and AE technology in terms of their applicability for diagnosing naturally degraded wheel set bearings. In addition, a novel fault diagnosis method based on the optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) and chirp Z-transform (CZT) is proposed to diagnose early composite fault defects in a wheel set bearing. The optimization CYCBD is adopted to enhance the fault-induced impact response and eliminate the interference of environmental noise, transmission path, and signal attenuation. CZT is used to improve the frequency resolution and match the fault features accurately under a limited data length condition. Moreover, the efficiency of the proposed method is verified by the simulated bearing signal and the real datasets. The results show that the proposed method is effective in the detection of wheel set bearing faults compared with the minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) methods. This research is also the first to compare the effectiveness of applying AE and vibration technologies to diagnose a naturally degraded high-speed train bearing, particularly close to actual line operation conditions.

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