A DEEP LONG SHORT-TERM MEMORY NETWORK FOR BEARING FAULT DIAGNOSIS UNDER TIME-VARYING CONDITIONS

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

3 Citations (Scopus)

Abstract

The fault diagnosis of bearing in machinery system plays a vital role in ensuring the normal operating performance of system. Machine learning-based fault diagnosis using vibration measurement recently has become a prevailing approach, which aims at identifying the fault through exploring the correlation between the measurement and respective fault. Nevertheless, such correlation will become very complex for the practical scenario where the system is operated under time-varying conditions. To fulfill the reliable bearing fault diagnosis under time-varying condition, this study presents a tailored deep learning model, so called deep long short-term memory (LSTM) network. By fully exploiting the strength of this model in characterizing the temporal dependence of time-series vibration measurement, the negative consequence of time-varying conditions can be minimized, thereby improving the diagnosis performance. The published bearing dataset with various time-varying operating speeds is utilized in case illustrations to validate the effectiveness of proposed methodology.

Original languageEnglish
Title of host publication34th Conference on Mechanical Vibration and Sound (VIB)
PublisherAmerican Society of Mechanical Engineers(ASME)
ISBN (Electronic)9780791886311
DOIs
Publication statusPublished - Nov 2022
Externally publishedYes
EventASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2022 - St. Louis, United States
Duration: 14 Aug 202217 Aug 2022

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume10

Conference

ConferenceASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2022
Country/TerritoryUnited States
CitySt. Louis
Period14/08/2217/08/22

Keywords

  • bearing fault diagnosis
  • deep long short-term memory (LSTM) neural network
  • time-series vibration measurement
  • time-varying conditions

ASJC Scopus subject areas

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

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