Bearing health assessment based on Hilbert transform envelope analysis and cluster analysis

Xin Zhang, Jianmin Zhao, Xianglong Ni, Haiping Li and Fucheng Sun

International Journal of Reliability and Safety, 2019, vol. 13, issue 3, 151-165

Abstract: The rolling bearings are one of the most critical and prevalent elements in rotating machinery. It is necessary to develop a suitable health assessment method to prevent malfunctions and breakages during operation. In this paper, a novel health assessment method for bearings based on Hilbert transform envelope analysis and cluster analysis is proposed. The high-pass filter and Hilbert transform envelope analysis are used to enable the original signal to become smooth and gentle, so as to reduce the influence of noise. And the combination of time domain features parameters is chosen to evaluate the bearing health state. Then extracted feature parameters are clustered by using improved K-means algorithm. In this paper, bearing degradation data and fault data are used to prove the effectiveness of the method.

Keywords: cluster analysis; bearing; Hilbert transform; envelope analysis; high-pass filter; health assessment; feature parameter; K-means algorithm; degradation; rotating machinery. (search for similar items in EconPapers)
Date: 2019
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