Abstract
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.
Highlights
A high-speed EMU axle box bearing is one of the most crucial but vulnerable components, which rotates with a very high-speed, suffers a heavy load, and operates under various complex work conditions
This study explored a high-speed EMU axle box bearing fault early warning system by employing machine learning
All the data were collected in real-time by the WTDS
Summary
A high-speed EMU (electrical multiple unit) axle box bearing is one of the most crucial but vulnerable components, which rotates with a very high-speed, suffers a heavy load, and operates under various complex work conditions. It was found that four variables had a strong correlation with the axle box bearing temperature, which were train speed, motor traction power, ambient temperature, and train mass. All the data were collected in real-time by the WTDS The correlation coefficient and heat map are shown, which reveals that the axle box bearing temperature had a strong positive correlation with ambient temperature, carriage mass, motor traction power, and train speed. These five parameters were taken as the input of the presented algorithm
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