Understanding and Learning Discriminant Features based on Multiattention 1DCNN for Wheelset Bearing Fault Diagnosis

Recently, deep-learning-based fault diagnosis methods have been widely studied for rolling bearings. However, these neural networks are lack of interpretability for fault diagnosis tasks. That is, how to understand and learn discriminant fault features from complex monitoring signals remains a great challenge. Considering this challenge, this article explores the use of the attention mechanism in fault diagnosis networks and designs attention module by fully considering characteristics of rolling bearing faults to enhance fault-related features and to ignore irrelevant features. Powered by the proposed attention mechanism, a multiattention one-dimensional convolutional neural network (MA1DCNN) is further…

• NN
  National Natural Science Foundation of China

  Award: 61833002
• SJ
  Southwest Jiaotong University

  Award: TPL1608
• BJ
  Beijing Jiaotong University
• SK
  State Key Laboratory of Rail Traffic Control and Safety

  Award: RCS2018K002
• SK
  State Key Laboratory of Traction Power