Liu Lei, Song Dongli, Geng Zilin, Zheng Zejun
State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, China.
Sensors (Basel). 2020 Feb 4;20(3):823. doi: 10.3390/s20030823.
An axle box bearing is one of the most important components of high-speed EMUs (electric multiple units), which runs at a very fast speed, suffers a heavy load, and operates under various complex working conditions. Once a bearing fault occurs, it not only has an enormous impact on the railway system, but also poses a threat to personal safety. Therefore, there is significant value in studying a real-time fault early warning of a high-speed EMU axle box bearing. However, to our best knowledge, there are three obvious defects in the existing fault early warning methods used for high-speed EMU axle box bearings: (1) these methods based on vibration are extremely mature, but there are no vibration sensors installed in high-speed EMU axle box because it will greatly increase the manufacturing cost; (2) a TADS (trackside acoustic device system) can effectively detect early failures, but only a portion of railways are equipped with such a facility; and (3) an EMU-ODS (electric multiple unit onboard detection system) has reported numerous untimely warnings, along with warnings of frequent occurrence being missed. Whereupon, a method is proposed to realize the fault early warning of an axle box bearing without installing a vibration sensor on the high-speed EMU in service, namely a MLSTM-iForest (multilayer long short-term memory-isolation forest). First, the time-series data of the temperature-related variables of the axle box bearing is used as the input of MLSTM to predict the axle box bearing temperature in the future. Then, the deviation index of the predicted axle box bearing temperature is calculated. Finally, the deviation index is input into an iForest algorithm for unsupervised classification to realize the fault early warning of an axle box bearing. Experimental results on high-speed EMU operation data sets demonstrated the availability and feasibility of the presented method toward achieving early fault warnings of a high-speed EMU axle box bearing.
轴箱轴承是高速动车组最重要的部件之一，高速动车组运行速度极快，负载沉重，且在各种复杂工况下运行。一旦轴承出现故障，不仅会对铁路系统产生巨大影响，还会对人身安全构成威胁。因此，研究高速动车组轴箱轴承的实时故障预警具有重要价值。然而，据我们所知，现有的用于高速动车组轴箱轴承的故障预警方法存在三个明显缺陷：（1）这些基于振动的方法非常成熟，但高速动车组轴箱未安装振动传感器，因为这会大幅增加制造成本；（2）轨边声学装置系统（TADS）能有效检测早期故障，但只有部分铁路配备了这种设施；（3）动车组车载检测系统（EMU-ODS）报告了大量不及时的预警，同时还存在频繁漏报的情况。于是，提出了一种在运行中的高速动车组上不安装振动传感器来实现轴箱轴承故障预警的方法，即多层长短期记忆-孤立森林（MLSTM-iForest）。首先，将轴箱轴承温度相关变量的时间序列数据作为MLSTM的输入，以预测未来轴箱轴承温度。然后，计算预测的轴箱轴承温度的偏差指数。最后，将偏差指数输入孤立森林算法进行无监督分类，以实现轴箱轴承的故障预警。基于高速动车组运行数据集的实验结果证明了所提方法在实现高速动车组轴箱轴承早期故障预警方面的有效性和可行性。
