Automatic bearing fault diagnosis using particle swarm clustering and Hidden Markov Model

• This paper proposes an automatic fault diagnosis algorithm for rolling bearing defects.
• The classification algorithm was Hidden Markov Model optimized with swarm clustering.
• The features were defect harmonics extracted using wavelet kurtogram and cepstral liftering.
• The bearing fault vibration data was obtained from Case Western Reserve University.
• Sensitivity and specificity of 98.02% and 96.03% were achieved on the test data.

Ball bearings are integral elements in most rotating manufacturing machineries. While detecting defective bearing is relatively straightforward, discovering the source of defect requires advanced signal processing techniques. This paper proposes an automatic bearing defect diagnosis method based on Swarm Rapid Centroid Estimation (SRCE) and Hidden Markov Model (HMM). Using the defect frequency signatures extracted with Wavelet Kurtogram and Cepstral Liftering, SRCE+HMM achieved on average the sensitivity, specificity, and error rate of 98.02%, 96.03%, and 2.65%, respectively, on the bearing fault vibration data provided by Case School of Engineering of the Case Western Reserve University (CSE) which warrants further investigation.

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