Fault Diagnosis of Wind Turbine Gearbox Based on Modified Hierarchical Fluctuation Dispersion Entropy of Tan-Sigmoid Mapping
Vibration monitoring and analysis are important methods in wind turbine gearbox fault diagnosis, and determining how to extract fault characteristics from the vibration signal is of primary importance. This paper presents a fault diagnosis approach based on modified hierarchical fluctuation dispersi...
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ftdoajarticles:oai:doaj.org/article:3e199153a5284259ab0570f660c05ccf 2024-09-15T18:25:45+00:00 Fault Diagnosis of Wind Turbine Gearbox Based on Modified Hierarchical Fluctuation Dispersion Entropy of Tan-Sigmoid Mapping Xiang Wang Yang Du 2024-06-01T00:00:00Z https://doi.org/10.3390/e26060507 https://doaj.org/article/3e199153a5284259ab0570f660c05ccf EN eng MDPI AG https://www.mdpi.com/1099-4300/26/6/507 https://doaj.org/toc/1099-4300 doi:10.3390/e26060507 1099-4300 https://doaj.org/article/3e199153a5284259ab0570f660c05ccf Entropy, Vol 26, Iss 6, p 507 (2024) gear box fault diagnosis tan-sigmoid mapping modified hierarchical fluctuation dispersion entropy support vector machine Science Q Astrophysics QB460-466 Physics QC1-999 article 2024 ftdoajarticles https://doi.org/10.3390/e26060507 2024-08-05T17:49:06Z Vibration monitoring and analysis are important methods in wind turbine gearbox fault diagnosis, and determining how to extract fault characteristics from the vibration signal is of primary importance. This paper presents a fault diagnosis approach based on modified hierarchical fluctuation dispersion entropy of tan-sigmoid mapping (MHFDE_TANSIG) and northern goshawk optimization–support vector machine (NGO–SVM) for wind turbine gearboxes. The tan-sigmoid (TANSIG) mapping function replaces the normal cumulative distribution function (NCDF) of the hierarchical fluctuation dispersion entropy (HFDE) method. Additionally, the hierarchical decomposition of the HFDE method is improved, resulting in the proposed MHFDE_TANSIG method. The vibration signals of wind turbine gearboxes are analyzed using the MHFDE_TANSIG method to extract fault features. The constructed fault feature set is used to intelligently recognize and classify the fault type of the gearboxes with the NGO–SVM classifier. The fault diagnosis methods based on MHFDE_TANSIG and NGO–SVM are applied to the experimental data analysis of gearboxes with different operating conditions. The results show that the fault diagnosis model proposed in this paper has the best performance with an average accuracy rate of 97.25%. Article in Journal/Newspaper Northern Goshawk Directory of Open Access Journals: DOAJ Articles Entropy 26 6 507 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
gear box fault diagnosis tan-sigmoid mapping modified hierarchical fluctuation dispersion entropy support vector machine Science Q Astrophysics QB460-466 Physics QC1-999 |
spellingShingle |
gear box fault diagnosis tan-sigmoid mapping modified hierarchical fluctuation dispersion entropy support vector machine Science Q Astrophysics QB460-466 Physics QC1-999 Xiang Wang Yang Du Fault Diagnosis of Wind Turbine Gearbox Based on Modified Hierarchical Fluctuation Dispersion Entropy of Tan-Sigmoid Mapping |
topic_facet |
gear box fault diagnosis tan-sigmoid mapping modified hierarchical fluctuation dispersion entropy support vector machine Science Q Astrophysics QB460-466 Physics QC1-999 |
description |
Vibration monitoring and analysis are important methods in wind turbine gearbox fault diagnosis, and determining how to extract fault characteristics from the vibration signal is of primary importance. This paper presents a fault diagnosis approach based on modified hierarchical fluctuation dispersion entropy of tan-sigmoid mapping (MHFDE_TANSIG) and northern goshawk optimization–support vector machine (NGO–SVM) for wind turbine gearboxes. The tan-sigmoid (TANSIG) mapping function replaces the normal cumulative distribution function (NCDF) of the hierarchical fluctuation dispersion entropy (HFDE) method. Additionally, the hierarchical decomposition of the HFDE method is improved, resulting in the proposed MHFDE_TANSIG method. The vibration signals of wind turbine gearboxes are analyzed using the MHFDE_TANSIG method to extract fault features. The constructed fault feature set is used to intelligently recognize and classify the fault type of the gearboxes with the NGO–SVM classifier. The fault diagnosis methods based on MHFDE_TANSIG and NGO–SVM are applied to the experimental data analysis of gearboxes with different operating conditions. The results show that the fault diagnosis model proposed in this paper has the best performance with an average accuracy rate of 97.25%. |
format |
Article in Journal/Newspaper |
author |
Xiang Wang Yang Du |
author_facet |
Xiang Wang Yang Du |
author_sort |
Xiang Wang |
title |
Fault Diagnosis of Wind Turbine Gearbox Based on Modified Hierarchical Fluctuation Dispersion Entropy of Tan-Sigmoid Mapping |
title_short |
Fault Diagnosis of Wind Turbine Gearbox Based on Modified Hierarchical Fluctuation Dispersion Entropy of Tan-Sigmoid Mapping |
title_full |
Fault Diagnosis of Wind Turbine Gearbox Based on Modified Hierarchical Fluctuation Dispersion Entropy of Tan-Sigmoid Mapping |
title_fullStr |
Fault Diagnosis of Wind Turbine Gearbox Based on Modified Hierarchical Fluctuation Dispersion Entropy of Tan-Sigmoid Mapping |
title_full_unstemmed |
Fault Diagnosis of Wind Turbine Gearbox Based on Modified Hierarchical Fluctuation Dispersion Entropy of Tan-Sigmoid Mapping |
title_sort |
fault diagnosis of wind turbine gearbox based on modified hierarchical fluctuation dispersion entropy of tan-sigmoid mapping |
publisher |
MDPI AG |
publishDate |
2024 |
url |
https://doi.org/10.3390/e26060507 https://doaj.org/article/3e199153a5284259ab0570f660c05ccf |
genre |
Northern Goshawk |
genre_facet |
Northern Goshawk |
op_source |
Entropy, Vol 26, Iss 6, p 507 (2024) |
op_relation |
https://www.mdpi.com/1099-4300/26/6/507 https://doaj.org/toc/1099-4300 doi:10.3390/e26060507 1099-4300 https://doaj.org/article/3e199153a5284259ab0570f660c05ccf |
op_doi |
https://doi.org/10.3390/e26060507 |
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Entropy |
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26 |
container_issue |
6 |
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507 |
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1810466231739744256 |