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Diagnosis of compound faults of rolling bearings through adaptive maximum correlated kurtosis deconvolution

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Abstract

This paper proposes a new diagnosis method based on Adaptive maximum correlated kurtosis deconvolution (AMCKD) for accurate identification of compound faults of rolling bearings. The AMCKD method combines the powerful capability of cuckoo search algorithm for global optimization with the advantage of Maximum correlated kurtosis deconvolution (MCKD) for impact signal extraction. In contrast to traditional methods, such as direct envelop spectrum, Discrete wavelet transform (DWT), and empirical mode decomposition, the proposed method extracts each fault signal related to the single failed part from the compound fault signals and effectively separates the coupled fault features. First, the original signal is processed using AMCKD method. Demodulation operation is then performed on the obtained single fault signal, and the envelope spectrum is calculated to identify the characteristic frequency information. Verification is performed on simulated and experimental signals. Results show that the proposed method is more suitable for detecting compound faults in rolling bearings compared with traditional methods. This research provides a basis for improving the monitoring and diagnosis precision of rolling bearings.

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Authors and Affiliations

  1. School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, 071000, China

    Guiji Tang, Xiaolong Wang & Yuling He

Authors
  1. Guiji Tang
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  2. Xiaolong Wang
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  3. Yuling He
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Corresponding author

Correspondence to Yuling He.

Additional information

Guiji Tang received his B.S. degree in mechanical design and manufacturing, M.S. degree in power plant engineering, and Ph.D. degree in thermal engineering, in 1983, 1991 and 1999, respectively, from North China Electric Power University, China. His main research interests include vibration monitoring and control, fault diagnosis on rotating machinery, etc. Prof. Tang is the head of the Vibration Engineering Institute of Hebei Province, China, the secretary general of the Testing Technology Research Society of Mechanical Engineering for National Universities, and the secretary general of the Special Committee on Dynamic Testing, Chinese Vibration Engineering Society.

Xiaolong Wang received his B.S. degree in mechanical engineering and automation from North China Electric Power University, Baoding, Hebei Province, China, in 2011. Currently, he is a doctoral candidate in power machinery and engineering at North China Electric Power University. His main research interests include signal processing, pattern recognition, and fault diagnosis and prognosis.

Yuling He received two B.S. degrees in mechanical manufacturing and electrical engineering in 2007, M.S. degree in mechatronics engineering in 2009, and Ph.D. degree in power machinery in 2012 from North China Electric Power University, China. His main research interests include condition monitoring and fault diagnosis on large power equipments, signal processing, and testing technology. Dr. He is the corresponding author of this paper. heyuling1@163.com, No.234 mail box, North China Electric Power University, Huadian Road, Baoding City, Hebei Province, China. Postal code: 071003, phone: 86-13933976956, fax: 86-0312-7525018.

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Tang, G., Wang, X. & He, Y. Diagnosis of compound faults of rolling bearings through adaptive maximum correlated kurtosis deconvolution. J Mech Sci Technol 30, 43–54 (2016). https://doi.org/10.1007/s12206-015-1206-7

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  • DOI: https://doi.org/10.1007/s12206-015-1206-7

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