Fault Diagnosis of Induction Motor Bearing Using Cepstrum-based Preprocessing and Ensemble Learning Algorithm

Kangkan Bhakta, Niloy Sikder, Abdullah Al Nahid, M M Manjurul Islam

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

14 Citations (Scopus)

Abstract

Monitoring the condition of rolling element bearing and diagnosing their faults are cumbrous jobs. Fortunately, we have machines to do the burdensome task for us. The contemporary development in the field of machine learning allows us not only to extract features from fault signals accurately but to analyze them and predict future bearing faults almost accurately as well in a systematic manner. Utilizing an ensemble learning method named Gradient Boosting (GB) our paper proposes a technique to previse future fault classes based on the data obtained from analyzing the recorded fault data. To demonstrate the cogency of the method, we applied it on the REB fault data provided by the Case Western Reserve University (CWRU) Lab. Employing this supervised learning algorithm after preprocessing the fault signals using real cepstrum analysis, we can detect and prefigure different types of bearing faults with a staggering 99.58% accuracy.
Original languageEnglish
Title of host publicationInternational Conference on Electrical, Computer and Communication Engineering (ECCE)
ISBN (Electronic)978-1-5386-9111-3
DOIs
Publication statusPublished (in print/issue) - 2019

Keywords

  • ensemble learning
  • fault diagnosis
  • cepstrum analysis
  • gradient boosting
  • feature extraction
  • machine learning

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