Long-range Dependencies Learning Based on Non-Local 1D-Convolutional Neural Network for Rolling Bearing Fault Diagnosis

Authors

  • Huan Wang School of Mechanical and Electrical Engineering and the Glasgow College, University of Electronic Science and Technology of China, China https://orcid.org/0000-0002-1403-5314
  • Zhiliang Liu State Key Laboratory of Traction Power, Southwest Jiaotong University &School of Mechanical and Electrical Engineering and the Glasgow College, University of Electronic Science and Technology of China,China https://orcid.org/0000-0002-4133-8230
  • Ting Ai State Key Laboratory of Traction Power, Southwest Jiaotong University, China

DOI:

https://doi.org/10.37965/jdmd.2022.53

Keywords:

Rolling Bearing, Fault Diagnosis, Convolutional Neural Network, Long-range Dependencies Learning.

Abstract

In the field of data-driven bearing fault diagnosis, convolutional neural network (CNN) has been widely researched and applied due to its superior feature extraction and classification ability. However, the convolutional operation could only process a local neighborhood at a time and thus lack ability of capturing long-range dependencies. Therefore, building an efficient learning method for long-range dependencies is crucial to comprehend and express signal features considering that the vibration signals obtained in a real industrial environment always have strong instability, periodicity, and temporal correlation. This paper introduces non-local mean to the CNN and presents 1D non-local block (1D-NLB) to extract long-range dependencies. The 1D-NLB computes the response at a position as a weighted average value of the features at all positions. Based on it, we propose a non-local 1D convolutional neural network (NL-1DCNN) aiming at rolling bearing fault diagnosis. Furthermore, the 1D-NLB could be simply plugged into most existing deep learning architecture to improve their fault diagnosis ability. Under multiple noise conditions, the 1D-NLB improves the performance of the CNN on the wheelset bearing dataset of high-speed train and the Case Western Reserve University bearing dataset. The experiment results show that the NL-1DCNN exhibits superior results compared with six state-of-the-art fault diagnosis methods.

Downloads

Published

2022-04-12

How to Cite

Wang, H., Liu, Z., & Ai, T. (2022). Long-range Dependencies Learning Based on Non-Local 1D-Convolutional Neural Network for Rolling Bearing Fault Diagnosis. Journal of Dynamics, Monitoring and Diagnostics, 1(3), 148–159. https://doi.org/10.37965/jdmd.2022.53

Issue

Section

Special Issue( Machine Learning and AI in Fault Diagnostics)