Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults

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 tra...

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Autores principales:	Xiaoan Yan, Daoming She, Yadong Xu, Minping Jia
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	morphological filtering multiscale Lempel–Ziv complexity softmax wind turbine gearbox fault diagnosis Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/da2a3d3108314b599b5f54f669907a17
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spelling	oai:doaj.org-article:da2a3d3108314b599b5f54f669907a172021-11-25T17:29:05ZApplication of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults10.3390/e231113721099-4300https://doaj.org/article/da2a3d3108314b599b5f54f669907a172021-10-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1372https://doaj.org/toc/1099-4300Wind 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)).Xiaoan YanDaoming SheYadong XuMinping JiaMDPI AGarticlemorphological filteringmultiscale Lempel–Ziv complexitysoftmaxwind turbine gearboxfault diagnosisScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1372, p 1372 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	morphological filtering multiscale Lempel–Ziv complexity softmax wind turbine gearbox fault diagnosis Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	morphological filtering multiscale Lempel–Ziv complexity softmax wind turbine gearbox fault diagnosis Science Q Astrophysics QB460-466 Physics QC1-999 Xiaoan Yan Daoming She Yadong Xu Minping Jia Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults
description	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)).
format	article
author	Xiaoan Yan Daoming She Yadong Xu Minping Jia
author_facet	Xiaoan Yan Daoming She Yadong Xu Minping Jia
author_sort	Xiaoan Yan
title	Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults
title_short	Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults
title_full	Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults
title_fullStr	Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults
title_full_unstemmed	Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults
title_sort	application of generalized composite multiscale lempel–ziv complexity in identifying wind turbine gearbox faults
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/da2a3d3108314b599b5f54f669907a17
work_keys_str_mv	AT xiaoanyan applicationofgeneralizedcompositemultiscalelempelzivcomplexityinidentifyingwindturbinegearboxfaults AT daomingshe applicationofgeneralizedcompositemultiscalelempelzivcomplexityinidentifyingwindturbinegearboxfaults AT yadongxu applicationofgeneralizedcompositemultiscalelempelzivcomplexityinidentifyingwindturbinegearboxfaults AT minpingjia applicationofgeneralizedcompositemultiscalelempelzivcomplexityinidentifyingwindturbinegearboxfaults
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Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults

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