Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults
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School of Mechatronics Engineering, Nanjing Forestry University, Nanjing 210037, China
2
School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China
3
School of Mechanical Engineering, Southeast University, Nanjing 211189, China
*
Author to whom correspondence should be addressed.
Academic Editors: José María Amigó and Piergiulio Tempesta
Entropy 2021, 23(11), 1372; https://doi.org/10.3390/e23111372
Received: 18 August 2021
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Revised: 15 October 2021
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Accepted: 18 October 2021
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Published: 20 October 2021
(This article belongs to the Special Issue Information Geometry, Complexity Measures and Data Analysis)
Wind turbine gearboxes operate in harsh environments; therefore, the resulting gear vibration signal has characteristics of strong nonlinearity, is non-stationary, and has a low signal-to-noise ratio, which indicates that it is difficult to identify wind turbine gearbox faults effectively by the traditional methods. To solve this problem, this paper proposes a new fault diagnosis method for wind turbine gearboxes based on generalized composite multiscale Lempel–Ziv complexity (GCMLZC). Within the proposed method, an effective technique named multiscale morphological-hat convolution operator (MHCO) is firstly presented to remove the noise interference information of the original gear vibration signal. Then, the GCMLZC of the filtered signal was calculated to extract gear fault features. Finally, the extracted fault features were input into softmax classifier for automatically identifying different health conditions of wind turbine gearboxes. The effectiveness of the proposed method was validated by the experimental and engineering data analysis. The results of the analysis indicate that the proposed method can identify accurately different gear health conditions. Moreover, the identification accuracy of the proposed method is higher than that of traditional multiscale Lempel–Ziv complexity (MLZC) and several representative multiscale entropies (e.g., multiscale dispersion entropy (MDE), multiscale permutation entropy (MPE) and multiscale sample entropy (MSE)).
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Keywords:
morphological filtering; multiscale Lempel–Ziv complexity; softmax; wind turbine gearbox; fault diagnosis
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MDPI and ACS Style
Yan, X.; She, D.; Xu, Y.; Jia, M. Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults. Entropy 2021, 23, 1372. https://doi.org/10.3390/e23111372
AMA Style
Yan X, She D, Xu Y, Jia M. Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults. Entropy. 2021; 23(11):1372. https://doi.org/10.3390/e23111372
Chicago/Turabian StyleYan, Xiaoan, Daoming She, Yadong Xu, and Minping Jia. 2021. "Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults" Entropy 23, no. 11: 1372. https://doi.org/10.3390/e23111372
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