期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 69, 期 3, 页码 2513-2524出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2019.2955221
关键词
Fault diagnosis; Vibrations; Data models; Gears; Monitoring; Temperature sensors; Multi-sensor data; Automatic fault diagnosis; Bogie; End-to-End model; CNN
资金
- National Key Research and Development Program of China [2016YFB1200401]
- National Key Technology RD Program [2015BAG12B01-06]
- State Key Laboratory of Rail Traffic Control and Safety [RCS2016K005]
With the improvement of sensor techniques, and the urgent requirement of automatic fault diagnosis technologies, the intelligent perception system on high speed train is more popular than ever before. It records the devices' state information through a sensor network, and services for further analysis. However, Traditional machine learning algorithms are usually constrained by massive multi-sensor data and knowledge-based feature extraction in fault diagnosis. Therefore, this paper extended fault diagnosis methodology into tensor space to deal with multi-sensor monitoring data and take full use of available information. Moreover, the convolutional neural network (CNN) is used for automatic feature learning and classification without human intervention. The effectiveness and efficiency are validated by dataset of rolling element bearings obtained in lab and real-use case. Three features can be highlighted. First of all, the proposed model showed a good adaptability and high efficiency under various working condition by taking full use of the multi-sensor data. It has powerful ability in accuracy and convergence speed. Secondly, it is not as sensitive to data quantity as other deep learning algorithms do. Such superior characteristic made the model more suitable for practical application, because of the insufficient failure data. At last, it is an intelligent End-to-End model, performing automatic fault diagnosis without manual intervention and suitable for real-use case.
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