Machine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data: A Review

The most important parts of rotating machinery are the rolling bearings. Finding bearing faults in time can avoid affecting the operation of the entire equipment. The data-driven fault diagnosis technology of bearings has recently become a research hotspot, and the starting point of research is ofte...

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Autores principales:	Xiao Zhang, Boyang Zhao, Yun Lin
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Fault diagnosis machine learning feature selection classifier CWRU Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/841b2e68eb404344a6c6e1c51b282102
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id	oai:doaj.org-article:841b2e68eb404344a6c6e1c51b282102
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spelling	oai:doaj.org-article:841b2e68eb404344a6c6e1c51b2821022021-11-26T00:01:39ZMachine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data: A Review2169-353610.1109/ACCESS.2021.3128669https://doaj.org/article/841b2e68eb404344a6c6e1c51b2821022021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9617588/https://doaj.org/toc/2169-3536The most important parts of rotating machinery are the rolling bearings. Finding bearing faults in time can avoid affecting the operation of the entire equipment. The data-driven fault diagnosis technology of bearings has recently become a research hotspot, and the starting point of research is often the acquisition of vibration signals. There are many public data sets for rolling bearings. Among them, the most widely used public dataset is Case Western Reserve University bearing center (CWRU). This paper will start from the CWRU data set, compare and analyze some basic methods of machine learning based rolling bearing fault diagnosis, and summarize the characteristics of CWRU. First, we give a comprehensive introduction to CWRU and summarize the results achieved. After that, the basic methods and principles of machine learning based rolling bearing fault diagnosis were summarized. Finally, we conduct experiments and analyze experimental results. This paper will have certain guiding significance for the future use of CWRU for machine learning based rolling bearing fault diagnosis.Xiao ZhangBoyang ZhaoYun LinIEEEarticleFault diagnosismachine learningfeature selectionclassifierCWRUElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 155598-155608 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Fault diagnosis machine learning feature selection classifier CWRU Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Fault diagnosis machine learning feature selection classifier CWRU Electrical engineering. Electronics. Nuclear engineering TK1-9971 Xiao Zhang Boyang Zhao Yun Lin Machine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data: A Review
description	The most important parts of rotating machinery are the rolling bearings. Finding bearing faults in time can avoid affecting the operation of the entire equipment. The data-driven fault diagnosis technology of bearings has recently become a research hotspot, and the starting point of research is often the acquisition of vibration signals. There are many public data sets for rolling bearings. Among them, the most widely used public dataset is Case Western Reserve University bearing center (CWRU). This paper will start from the CWRU data set, compare and analyze some basic methods of machine learning based rolling bearing fault diagnosis, and summarize the characteristics of CWRU. First, we give a comprehensive introduction to CWRU and summarize the results achieved. After that, the basic methods and principles of machine learning based rolling bearing fault diagnosis were summarized. Finally, we conduct experiments and analyze experimental results. This paper will have certain guiding significance for the future use of CWRU for machine learning based rolling bearing fault diagnosis.
format	article
author	Xiao Zhang Boyang Zhao Yun Lin
author_facet	Xiao Zhang Boyang Zhao Yun Lin
author_sort	Xiao Zhang
title	Machine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data: A Review
title_short	Machine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data: A Review
title_full	Machine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data: A Review
title_fullStr	Machine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data: A Review
title_full_unstemmed	Machine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data: A Review
title_sort	machine learning based bearing fault diagnosis using the case western reserve university data: a review
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/841b2e68eb404344a6c6e1c51b282102
work_keys_str_mv	AT xiaozhang machinelearningbasedbearingfaultdiagnosisusingthecasewesternreserveuniversitydataareview AT boyangzhao machinelearningbasedbearingfaultdiagnosisusingthecasewesternreserveuniversitydataareview AT yunlin machinelearningbasedbearingfaultdiagnosisusingthecasewesternreserveuniversitydataareview
_version_	1718409980364718080

Machine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data: A Review

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