Bearing Fault Reconstruction Diagnosis Method Based on ResNet-152 with Multi-Scale Stacked Receptive Field.

Hu Yu,Hua Wang,Xiaodong Miao

doi:10.3390/s22051705

Abstract

The axle box in the bogie system of subway trains is a key component connecting primary damper and the axle. In order to extract deep features and large-scale fault features for rapid diagnosis, a novel fault reconstruction characteristics classification method based on deep residual network with a multi-scale stacked receptive field for rolling bearings of a subway train axle box is proposed. Firstly, multi-layer stacked convolutional kernels and methods to insert them into ultra-deep residual networks are developed. Then, the original vibration signals of four fault characteristics acquired are reconstructed with a Gramian angular summation field and trainable large-scale 2D time-series images are obtained. In the end, the experimental results show that ResNet-152-MSRF has a low complexity of network structure, less trainable parameters than general convolutional neural networks, and no significant increase in network parameters and calculation time after embedding multi-layer stacked convolutional kernels. Moreover, there is a significant improvement in accuracy compared to lower depths, and a slight improvement in accuracy compared to networks than unembedded multi-layer stacked convolutional kernels.

Highlights

Subway trains are integral to traffic systems, modernization, and urban culture [1,2,3]
The entire device consists of a motor, an acceleration sensor, a magnetic brake and a subway train axle box
The load and speed conditions of the bearings in the axle box are determined by the motor, and the sampling frequency is determined by the acceleration sensor

Summary

Introduction

Subway trains are integral to traffic systems, modernization, and urban culture [1,2,3]. Because these axle boxes of a subway train support the whole weight of the subway vehicle and ensure the reliability of a subway train [4,5], and the rolling bearings are the vitally important component to transfer loads and torque through which are filtered by an air spring to shaft. Fast and accurate fault diagnosis of axle box bearings can be used to maintain the smooth operation of urban rail transit and extend service time as well as ensure travel safety. Vibration-based signal analysis methods detect faults by extracting fault-related vibration components and characteristic frequency. It is hard to extract pure fault-related vibration signal by traditional vibration-based signal analysis

Methods

Results

Conclusion