Vibration-based bearing fault detection and diagnosis via image recognition technique under constant and variable speed conditions

Moussa Hamadache, Dongik Lee, Emiliano Mucchi, Giorgio Dalpiaz

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
72 Downloads (Pure)

Abstract

This paper addresses the application of an image recognition technique for the detection and diagnosis of ball bearing faults in rotating electrical machines (REMs). The conventional bearing fault detection and diagnosis (BFDD) methods rely on extracting different features from either waveforms or spectra of vibration signals to detect and diagnose bearing faults. In this paper, a novel vibration-based BFDD via a probability plot (ProbPlot) image recognition technique under constant and variable speed conditions is proposed. The proposed technique is based on the absolute value principal component analysis (AVPCA), namely, ProbPlot via image recognition using the AVPCA (ProbPlot via IR-AVPCA) technique. A comparison of the features (images) obtained: (1) directly in the time domain from the original raw data of the vibration signals; (2) by capturing the Fast Fourier Transformation (FFT) of the vibration signals; or (3) by generating the probability plot (ProbPlot) of the vibration signals as proposed in this paper, is considered. A set of realistic bearing faults (i.e., outer-race fault, inner-race fault, and balls fault) are experimentally considered to evaluate the performance and effectiveness of the proposed ProbPlot via the IR-AVPCA method.
Original languageEnglish
Article number1392
Number of pages19
JournalApplied Sciences
Volume8
Issue number8
DOIs
Publication statusPublished - 17 Aug 2018

Keywords

  • Bearing fault detection and diagnosis (BFDD)
  • Vibration signal; probability plot (ProbPlot)
  • Image recognition
  • Absolute value principal component analysis (AVPCA)

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