A Deep Ensemble Learning Model for Rolling Bearing Fault Diagnosis

Ruixin Wang, Hongkai Jiang, Zhenning Li, Yunpeng Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Rolling bearing is an important component of rotating machinery. The accurate fault diagnosis of rolling bearing is very important. Nowadays, experts began to explore the combination strategies of deep learning networks. Ensemble learning can achieve higher recognition accuracy by combining multiple models. Therefore, a deep ensemble learning model is proposed for rolling bearing fault diagnosis. Firstly, four different Convolutional Neural Networks (CNN) networks are constructed as the base-learners. Secondly, the 4-fold cross validation method is adopted for training the base-learner. Finally, the Artificial Neural Network (ANN) is used as the meta-learner and the stacking method is used for model ensemble. The proposed method can get high classification accuracy and accurately identify all kinds of faults.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Prognostics and Health Management, ICPHM 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages133-136
Number of pages4
ISBN (Electronic)9781665466158
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Prognostics and Health Management, ICPHM 2022 - Detroit, United States
Duration: 6 Jun 20228 Jun 2022

Publication series

Name2022 IEEE International Conference on Prognostics and Health Management, ICPHM 2022

Conference

Conference2022 IEEE International Conference on Prognostics and Health Management, ICPHM 2022
Country/TerritoryUnited States
CityDetroit
Period6/06/228/06/22

Keywords

  • ensemble learning
  • fault diagnosis
  • rolling bearing
  • stacking

Fingerprint

Dive into the research topics of 'A Deep Ensemble Learning Model for Rolling Bearing Fault Diagnosis'. Together they form a unique fingerprint.

Cite this