Intelligent Fault Diagnosis of the High-Speed Train With Big Data Based on Deep Neural Networks

• Published in: IEEE transactions on industrial informatics Vol. 13; no. 4; pp. 2106 - 2116
• Main Authors: Hu, Hexuan, Tang, Bo, Gong, Xuejiao, Wei, Wei, Wang, Huihui
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; Big Data; Biological neural networks; Bogies; Data management; Data mining; deep neural networks; diagnostic accuracy rate; Diagnostic systems; Fault diagnosis; Faults; Feature extraction; High speed rail; high-speed train with big data; Mechanical properties; Monitoring; Neural networks; Reliability; Signal processing; Trains; Undercarriages
• DOI: 10.1109/TII.2017.2683528
• ISSN: 1551-3203

Bogies are an important component of high-speed trains. The level of mechanical performance of bogies has a major influence on the safety and reliability of high-speed train. Therefore, conducting fault diagnoses on bogies with big data is very important. Fault mechanisms of bogies are very complex, and feature signals are nonobvious. For these reasons, fault information of bogies cannot be effectively extracted using the traditional signal processing method. Therefore, this paper adopted the deep neural network to recognize faults in bogies. The deep neural network offers numerous benefits in this context. Using deep neural networks, fault information in a signal spectrum can be extracted in a selfadaptive method. This technique is free of dependence on extensive signal processing knowledge and diagnostic experience. Compared with the traditional intelligent diagnosis method, the deep neural network can obtain a higher diagnostic accuracy. Additionally, the deep neural network does not depend on the sample size, and it can obtain high diagnostic accuracy even when the sample size is relatively small. It also achieves very high diagnostic accuracy applied to high-speed trains with different speeds and different faults, which shows that the method is extensively applicable. Furthermore, the recognition accuracy rate of the deep neural network under normal conditions can reach 100%. This method provides a new paradigm for fault diagnosis of the high-speed train with big data and plays an important role in this field.