A review: data driven-based fault diagnosis and RUL prediction of petroleum machinery and equipment

In this paper, an up-to-date overview is provided on the data driven-based fault diagnosis (FD) and remaining useful life (RUL) prediction problems of the petroleum machinery and equipment (PME). First, the FD and RUL prediction of five key components including bearings, gears, motors, pumps and pip...

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Autores principales:	Daan Ji, Chuang Wang, Jiahui Li, Hongli Dong
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	petroleum machinery and equipment fault diagnosis remaining useful life prediction mathematical statistics shallow learning deep learning Control engineering systems. Automatic machinery (General) TJ212-225 Systems engineering TA168
Acceso en línea:	https://doaj.org/article/aaac3d3ca8484b25b0a3cf91b59315c7
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id	oai:doaj.org-article:aaac3d3ca8484b25b0a3cf91b59315c7
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spelling	oai:doaj.org-article:aaac3d3ca8484b25b0a3cf91b59315c72021-11-04T15:51:53ZA review: data driven-based fault diagnosis and RUL prediction of petroleum machinery and equipment2164-258310.1080/21642583.2021.1992684https://doaj.org/article/aaac3d3ca8484b25b0a3cf91b59315c72021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/21642583.2021.1992684https://doaj.org/toc/2164-2583In this paper, an up-to-date overview is provided on the data driven-based fault diagnosis (FD) and remaining useful life (RUL) prediction problems of the petroleum machinery and equipment (PME). First, the FD and RUL prediction of five key components including bearings, gears, motors, pumps and pipelines are discussed by adopting mathematical statistics and shallow learning. Then, four kinds of widely-used DL models, i.e. deep neural networks, deep belief networks, convolution neural networks and recurrent neural networks, are surveyed, and the applications in the field of PME are highlighted. Finally, the possible challenges are proposed and some corresponding research directions in the future are presented.Daan JiChuang WangJiahui LiHongli DongTaylor & Francis Grouparticlepetroleum machinery and equipmentfault diagnosisremaining useful life predictionmathematical statisticsshallow learningdeep learningControl engineering systems. Automatic machinery (General)TJ212-225Systems engineeringTA168ENSystems Science & Control Engineering, Vol 9, Iss 1, Pp 724-747 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	petroleum machinery and equipment fault diagnosis remaining useful life prediction mathematical statistics shallow learning deep learning Control engineering systems. Automatic machinery (General) TJ212-225 Systems engineering TA168
spellingShingle	petroleum machinery and equipment fault diagnosis remaining useful life prediction mathematical statistics shallow learning deep learning Control engineering systems. Automatic machinery (General) TJ212-225 Systems engineering TA168 Daan Ji Chuang Wang Jiahui Li Hongli Dong A review: data driven-based fault diagnosis and RUL prediction of petroleum machinery and equipment
description	In this paper, an up-to-date overview is provided on the data driven-based fault diagnosis (FD) and remaining useful life (RUL) prediction problems of the petroleum machinery and equipment (PME). First, the FD and RUL prediction of five key components including bearings, gears, motors, pumps and pipelines are discussed by adopting mathematical statistics and shallow learning. Then, four kinds of widely-used DL models, i.e. deep neural networks, deep belief networks, convolution neural networks and recurrent neural networks, are surveyed, and the applications in the field of PME are highlighted. Finally, the possible challenges are proposed and some corresponding research directions in the future are presented.
format	article
author	Daan Ji Chuang Wang Jiahui Li Hongli Dong
author_facet	Daan Ji Chuang Wang Jiahui Li Hongli Dong
author_sort	Daan Ji
title	A review: data driven-based fault diagnosis and RUL prediction of petroleum machinery and equipment
title_short	A review: data driven-based fault diagnosis and RUL prediction of petroleum machinery and equipment
title_full	A review: data driven-based fault diagnosis and RUL prediction of petroleum machinery and equipment
title_fullStr	A review: data driven-based fault diagnosis and RUL prediction of petroleum machinery and equipment
title_full_unstemmed	A review: data driven-based fault diagnosis and RUL prediction of petroleum machinery and equipment
title_sort	review: data driven-based fault diagnosis and rul prediction of petroleum machinery and equipment
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/aaac3d3ca8484b25b0a3cf91b59315c7
work_keys_str_mv	AT daanji areviewdatadrivenbasedfaultdiagnosisandrulpredictionofpetroleummachineryandequipment AT chuangwang areviewdatadrivenbasedfaultdiagnosisandrulpredictionofpetroleummachineryandequipment AT jiahuili areviewdatadrivenbasedfaultdiagnosisandrulpredictionofpetroleummachineryandequipment AT honglidong areviewdatadrivenbasedfaultdiagnosisandrulpredictionofpetroleummachineryandequipment AT daanji reviewdatadrivenbasedfaultdiagnosisandrulpredictionofpetroleummachineryandequipment AT chuangwang reviewdatadrivenbasedfaultdiagnosisandrulpredictionofpetroleummachineryandequipment AT jiahuili reviewdatadrivenbasedfaultdiagnosisandrulpredictionofpetroleummachineryandequipment AT honglidong reviewdatadrivenbasedfaultdiagnosisandrulpredictionofpetroleummachineryandequipment
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A review: data driven-based fault diagnosis and RUL prediction of petroleum machinery and equipment

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