Home > Research > Publications & Outputs > Multi-scale deep intra-class transfer learning ...

Associated organisational unit

Electronic data

  • 1-s2.0-S0951832020305512-main

    Rights statement: This is the author’s version of a work that was accepted for publication in Reliability Engineering and System Safety. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Reliability Engineering and System Safety, 202, 2020 DOI: 10.1016/j.ress.2020.107050

    Accepted author manuscript, 4.3 MB, PDF document

    Available under license: CC BY-NC-ND

Links

Text available via DOI:

View graph of relations

Multi-scale deep intra-class transfer learning for bearing fault diagnosis

Research output: Contribution to journalJournal articlepeer-review

Published
  • X. Wang
  • C. Shen
  • M. Xia
  • D. Wang
  • J. Zhu
  • Z. Zhu
Close
Article number107050
<mark>Journal publication date</mark>1/10/2020
<mark>Journal</mark>Reliability Engineering and System Safety
Volume202
Number of pages15
Publication StatusPublished
Early online date3/06/20
<mark>Original language</mark>English

Abstract

The tremendous success of deep learning in machine fault diagnosis is dependent on the hypothesis that training and test datasets are subordinated to the same distribution. This subordination is difficult to meet in practical scenarios of industrial applications. On the one hand, the working conditions of rotating machinery can change easily. On the other hand, vibration data and labels are difficult to obtain to train a specific model for each working condition. In this study, we solve these problems by constructing a novel deep transfer learning model called multi-scale deep intra-class adaptation network, which first uses the modified ResNet-50 to extract low-level features and then constructs a multiple scale feature learner to analyze these low-level features at multiple scales and obtain high-level features as input for the classifier. Pseudo labels are then computed to shorten the conditional distribution distance of vibration data collected under different working loads for intra-class adaptation. The proposed method is validated using two datasets to recognize the bearing normal state, the inner race, the ball and outer race faults, and their fault degrees under four different working loads. The high-precision diagnosis results of 24 transfer learning experiments reveal the reliability and generalizability of the constructed model.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Reliability Engineering and System Safety. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Reliability Engineering and System Safety, 202, 2020 DOI: 10.1016/j.ress.2020.107050