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Weak fault detection for wind turbine bearing based on ACYCBD and IESB

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Abstract

To detect the rolling bearing fault of a wind turbine at incipient injury phase, a novel method combining adaptive cyclostationary blind deconvolution (ACYCBD) with instantaneous energy slice bispectrum (IESB) is proposed. Cuckoo search algorithm (CSA) is fused with cyclostationary blind deconvolution (CYCBD) to optimize cyclic frequency and filter length parameters, and the satisfied processing result is able to adaptively use this developed ACYCBD method. Besides, fusing the complementary superiorities of frequency weighted energy operator and slice bispectrum, a new frequency domain analysis method named IESB is put forward to identify the characteristic frequency components. During fault diagnosis, the deconvolution signal with higher signal to noise ratio is first separated from the collected signal by applying ACYCBD method. However, owing to the intense background noises, external interferences still remain in the deconvolution signal. Then the IESB is further used as a post-processing method, the residual noises are suppressed effectively, and the characteristic spectral lines are highlighted immensely. In the end, the incipient fault injury of wind turbine bearing can be diagnosed by analyzing the acquired instantaneous energy slice bispectrum. The analyzed results of wind turbine bearing fault signals from the experimental rig and the actual engineering testing field demonstrate the feasibility and the superiority of this novel detection method.

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Abbreviations

ACYCBD:

Adaptive cyclostationary blind deconvolution

CSA:

Cuckoo search algorithm

CYCBD:

Cyclostationary blind deconvolution

EMD:

Empirical mode decomposition

FCER:

Fault characteristic energy ratio

FWEO:

Frequency weighted energy operator

IESB:

Instantaneous energy slice bispectrum

MCKD:

Maximum correlated kurtosis deconvolution

MED:

Minimum entropy deconvolution

SNR:

Signal to noise ratio

SK:

Spectral kurtosis

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 51777074), the Natural Science Foundation of Hebei Province, China (No. E2019502047), and the Fundamental Research Funds for the Central Universities (No. 2018MS124 and No. 2017MS190).

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Authors and Affiliations

  1. Department of Mechanical Engineering, North China Electric Power University, Baoding, 071000, China

    Xiaolong Wang, Xiaoli Yan & Yuling He

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  1. Xiaolong Wang
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  3. Yuling He
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Corresponding author

Correspondence to Yuling He.

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Recommended by Editor No-cheol Park

Xiaolong Wang received the B.S. in Mechanical Engineering and Automation from North China Electric Power University, Baoding, China, in 2011, and the Ph.D. in Power Machinery and Engineering from North China Electric Power University, Beijing, China, in 2017, respectively. Since 2017, he has been a lecturer in Mechanical Engineering, North China Electric Power University. His main research interests include signal processing, pattern recognition, fault diagnosis and prognosis.

Xiaoli Yan received the B.E. in Measurement & Control Technology and Instrument and the M.S. in Instrument and Meter Engineering from Yanshan University, Qinhuangdao, China, in 2011 and 2014, respectively. She is currently a doctoral candidate in Energy, Power and Mechanical Engineering, North China Electric Power University. Her current research interests include mechanical condition monitoring, fault diagnosis, life prediction, and signal processing.

Yuling He received two B.S. degrees from North China Electric Power University, Baoding, China, in 2007: One in Mechanical Manufacturing, and the other in Electrical Engineering. He received the M.S. in Mechatronics Engineering and the Ph.D. in Power Machinery and Engineering from North China Electric Power University, Baoding, China in 2009 and 2012, respectively. He is currently an Associate Professor of Mechanical Engineering, North China Electric Power University. His main research interests include condition monitoring and fault diagnosis on large power equipments, signal processing, and testing technology.

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Wang, X., Yan, X. & He, Y. Weak fault detection for wind turbine bearing based on ACYCBD and IESB. J Mech Sci Technol 34, 1399–1413 (2020). https://doi.org/10.1007/s12206-020-0303-4

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  • DOI: https://doi.org/10.1007/s12206-020-0303-4

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