A Novel Fault Diagnosis for Rolling Element Bearings Based on Continuous Wavelet Transform and Hidden Markov Model

Abstract:

This paper presents a novel approach to detect the fault categories for rolling element bearings based on the continuous wavelet transform and hidden Markov models. With the Morlet wavelet transform, the effective fault information is extracted from the time-frequency domain of the vibration signals. Then, the wavelet coefficients are divided into multi-segments, and the infinity-norms of each segment is applied as the features to construct the observation vector of the hidden Markov models. Finally, the experimental results on bearing faults identification and isolation are illustrated.

• Periodical: Applied Mechanics and Materials (Volume 109)
• Pages: 313-317
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.109.313
• Online since: October 2011
• Authors: Rui Ge Zhang, Yong Hong Tan
• Keywords: Bearing Fault Diagnosis, Continuous Wavelet Transform, Hidden Markov Model (HMM)

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