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A Mechanism-Based Automatic Fault Diagnosis Method for Gearboxes

Convenient and fast fault diagnosis is the key to improving the service safety and maintenance efficiency of gearboxes. However, the environment and working conditions under complex service conditions are variable, and there is a lack of fault samples in engineering applications. These factors lead...

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Detalles Bibliográficos
Autores principales: Xu, Lei, Wang, Tiantian, Xie, Jingsong, Yang, Jinsong, Gao, Guangjun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9735839/
https://www.ncbi.nlm.nih.gov/pubmed/36501851
http://dx.doi.org/10.3390/s22239150
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author Xu, Lei
Wang, Tiantian
Xie, Jingsong
Yang, Jinsong
Gao, Guangjun
author_facet Xu, Lei
Wang, Tiantian
Xie, Jingsong
Yang, Jinsong
Gao, Guangjun
author_sort Xu, Lei
collection PubMed
description Convenient and fast fault diagnosis is the key to improving the service safety and maintenance efficiency of gearboxes. However, the environment and working conditions under complex service conditions are variable, and there is a lack of fault samples in engineering applications. These factors lead to difficulties in intelligent diagnosis methods based on machine learning, while traditional mechanism-based fault diagnosis requires high expertise and long time periods for the manual analysis of data. For the requirements of diagnostic convenience, an automatic fault diagnosis method for gearboxes is proposed in this paper. The method achieves accurate acquisition of rotational speed by constructing a rotational frequency search algorithm. The self-referencing characteristic frequency identification method is proposed to avoid manual signal analysis. On this basis, a framework of anti-interference automatic diagnosis is constructed to realize automatic diagnosis of gear faults. Finally, a gear fault experiment is carried out based on a high-fidelity experimental bench of bogie to verify the effectiveness of the proposed method. The proposed automatic diagnosis method does not rely on a large number of fault samples and avoids the need for diagnosis through professional knowledge, thus saving time for data analysis and promoting the application of fault diagnosis methods.
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spelling pubmed-97358392022-12-11 A Mechanism-Based Automatic Fault Diagnosis Method for Gearboxes Xu, Lei Wang, Tiantian Xie, Jingsong Yang, Jinsong Gao, Guangjun Sensors (Basel) Article Convenient and fast fault diagnosis is the key to improving the service safety and maintenance efficiency of gearboxes. However, the environment and working conditions under complex service conditions are variable, and there is a lack of fault samples in engineering applications. These factors lead to difficulties in intelligent diagnosis methods based on machine learning, while traditional mechanism-based fault diagnosis requires high expertise and long time periods for the manual analysis of data. For the requirements of diagnostic convenience, an automatic fault diagnosis method for gearboxes is proposed in this paper. The method achieves accurate acquisition of rotational speed by constructing a rotational frequency search algorithm. The self-referencing characteristic frequency identification method is proposed to avoid manual signal analysis. On this basis, a framework of anti-interference automatic diagnosis is constructed to realize automatic diagnosis of gear faults. Finally, a gear fault experiment is carried out based on a high-fidelity experimental bench of bogie to verify the effectiveness of the proposed method. The proposed automatic diagnosis method does not rely on a large number of fault samples and avoids the need for diagnosis through professional knowledge, thus saving time for data analysis and promoting the application of fault diagnosis methods. MDPI 2022-11-25 /pmc/articles/PMC9735839/ /pubmed/36501851 http://dx.doi.org/10.3390/s22239150 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xu, Lei
Wang, Tiantian
Xie, Jingsong
Yang, Jinsong
Gao, Guangjun
A Mechanism-Based Automatic Fault Diagnosis Method for Gearboxes
title A Mechanism-Based Automatic Fault Diagnosis Method for Gearboxes
title_full A Mechanism-Based Automatic Fault Diagnosis Method for Gearboxes
title_fullStr A Mechanism-Based Automatic Fault Diagnosis Method for Gearboxes
title_full_unstemmed A Mechanism-Based Automatic Fault Diagnosis Method for Gearboxes
title_short A Mechanism-Based Automatic Fault Diagnosis Method for Gearboxes
title_sort mechanism-based automatic fault diagnosis method for gearboxes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9735839/
https://www.ncbi.nlm.nih.gov/pubmed/36501851
http://dx.doi.org/10.3390/s22239150
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