SEMI-SUPERVISED AUTOENCODER WITH JOINT LOSS LEARNING FOR BEARING FAULT DETECTION

Kai Zhou, Yang Zhang, Jiong Tang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Timely and accurate bearing fault detection plays an important role in various industries. Data-driven deep learning methods have recently become a prevailing approach for bearing fault detection. Despite the success of deep learning, fault diagnosis performance is hinged upon the size of labeled data, the acquisition of which oftentimes is expensive in actual practice. Unlabeled data, on the other hand, are inexpensive. To fully utilize a large amount of unlabeled data together with limited labeled data to enhance fault detection performance, in this research, we develop a semi-supervised learning method built upon the autoencoder. In this method, a joint loss is established to account for the effects of both the labeled and unlabeled data, which is subsequently used to direct the backpropagation training. Systematic case studies using the Case Western Reserve University (CWRU) rolling bearing dataset are carried out, in which the effectiveness of this new method is verified by comparing it with other benchmark models.

Original languageEnglish
Title of host publication35th Conference on Mechanical Vibration and Sound (VIB)
PublisherAmerican Society of Mechanical Engineers(ASME)
ISBN (Electronic)9780791887400
DOIs
Publication statusPublished - Nov 2023
EventASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023 - Boston, United States
Duration: 20 Aug 202323 Aug 2023

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume12

Conference

ConferenceASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023
Country/TerritoryUnited States
CityBoston
Period20/08/2323/08/23

Keywords

  • autoencoder
  • deep learning
  • fault detection
  • joint loss
  • Rolling bearing
  • semi-supervised learning

ASJC Scopus subject areas

  • Mechanical Engineering
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Modelling and Simulation

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