Abstract
Compound fault characteristics in single-channel vibration signals of rolling bearings are difficult to separate. On the basis of improved harmonic wavelet packet decomposition and fast independent component analysis (FICA), this study proposes a new method to address this problem. First, a series of mutually independent frequency bands are obtained after harmonic wavelet packet decomposition of the initial vibration signal to satisfy the requirement that the number of observed signals must be larger than the number of source signals in the FICA algorithm. Second, the optimal frequency bands are selected based on the maximum kurtosis index and used as the input matrix of the FICA algorithm to separate the compound fault characteristics further. Lastly, accurate separation and extraction of the compound fault characteristics of the rolling bearings are realized. Results show that the proposed method can effectively separate the compound fault characteristics in the single-channel vibration signals of the bearings.
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This work is supported by the National Natural Science Foundation of China (No. 51965037).
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Hua Huang is a Professor at the School of Mechanical and Electrical Engineering in Lanzhou University of Technology. He obtained his Ph.D. from Tongji University, China, in 2011. His research interests include condition monitoring and fault diagnosis of mechanical equipment.
Wenhu Xue is a Postgraduate student at the School of Mechanical and Electrical Engineering in Lanzhou University of Technology. His research interests include condition monitoring and fault diagnosis of mechanical equipment.
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Huang, H., Xue, W. & Pang, Q. Separation method of rolling bearing compound fault characteristics based on Improved harmonic wavelet packet decomposition and fast ICA. J Mech Sci Technol 36, 3263–3276 (2022). https://doi.org/10.1007/s12206-022-0607-7
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DOI: https://doi.org/10.1007/s12206-022-0607-7