A Multi-Dimension End-to-End CNN Model for Rotating Devices Fault Diagnosis on High-Speed Train Bogie

作者：Linlin Kou, Yong Qin, Xuejun Zhao, Xin'an Chen · 发表于：IEEE Transactions on Vehicular Technology · 年份：2019 · DOI：10.1109/tvt.2019.2955221 · 被引用次数：87 · 研究领域：Machine Fault Diagnosis Techniques、Gear and Bearing Dynamics Analysis、Engineering Diagnostics and Reliability

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.