Vibration-Based Bearing Fault Detection and Diagnosis via Image Recognition Technique Under Constant and Variable Speed Conditions

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 s...

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Bibliographic Details
Published inApplied sciences Vol. 8; no. 8; p. 1392
Main Authors Hamadache, Moussa, Lee, Dongik, Mucchi, Emiliano, Dalpiaz, Giorgio
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2018
Subjects
absolute value principal component analysis (AVPCA)
Algorithms
Bearing
bearing fault detection and diagnosis (BFDD)
Deep learning
Diagnosis
Fault detection
Fault diagnosis
Fourier transforms
Frequency domain analysis
Handbooks
Historical account
Image detection
image recognition
Information systems
Parameters
probability plot (ProbPlot)
Statistical analysis
Statistical methods
Vibration
vibration signal
Wavelet transforms
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app8081392