Research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology

Recently, researchers are investing more fervently in fault diagnosis area of electrical machines. The users and manufacturers of these various efforts are strong to contain diagnostic features in software for improving reliability and scalability. Internet of Things (IoT) has grown immensely and co...

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Autores principales:	Zhang Xiaoran, Rane Kantilal Pitambar, Kakaravada Ismail, Shabaz Mohammad
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	industrial wireless sensor networks (iwsns) internet of things (iot) support vector machine fault diagnosis Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/ab2863de282a49bba405ac432cb5c233
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spelling	oai:doaj.org-article:ab2863de282a49bba405ac432cb5c2332021-12-05T14:10:57ZResearch on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology2192-80102192-802910.1515/nleng-2021-0019https://doaj.org/article/ab2863de282a49bba405ac432cb5c2332021-10-01T00:00:00Zhttps://doi.org/10.1515/nleng-2021-0019https://doaj.org/toc/2192-8010https://doaj.org/toc/2192-8029Recently, researchers are investing more fervently in fault diagnosis area of electrical machines. The users and manufacturers of these various efforts are strong to contain diagnostic features in software for improving reliability and scalability. Internet of Things (IoT) has grown immensely and contributing for the development of recent technological advancements in industries, medical and various environmental applications. It provides efficient processing power through cloud, and presents various new opportunities for industrial automation by implementing IoT and industrial wireless sensor networks. The process of regular monitoring enables early detection of machine faults and hence beneficial for Industrial automation by providing efficient process control. The performance of fault detection and its classification by implementing machine-learning algorithms highly dependent on the amount of features involved. The accuracy of classification will adversely affect by the dimensionality features increment. To address these problems, the proposed work presents the extraction of relevant features based on oriented sport vector machine (FO-SVM). The proposed algorithm is capable for extracting the most relevant feature set and hence presenting the accurate classification of faults accordingly. The extraction of most relevant features before the process of classification results in higher classification accuracy. Moreover it is observed that the lesser dimensionality of propose process consumes less time and more suitable for cloud. The experimental analysis based on the implementation of proposed approach provides and solution for the monitoring of machine condition and prediction of fault accurately based on cloud platform using industrial wireless sensor networks and IoT service.Zhang XiaoranRane Kantilal PitambarKakaravada IsmailShabaz MohammadDe Gruyterarticleindustrial wireless sensor networks (iwsns)internet of things (iot)support vector machinefault diagnosisEngineering (General). Civil engineering (General)TA1-2040ENNonlinear Engineering, Vol 10, Iss 1, Pp 245-254 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	industrial wireless sensor networks (iwsns) internet of things (iot) support vector machine fault diagnosis Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	industrial wireless sensor networks (iwsns) internet of things (iot) support vector machine fault diagnosis Engineering (General). Civil engineering (General) TA1-2040 Zhang Xiaoran Rane Kantilal Pitambar Kakaravada Ismail Shabaz Mohammad Research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology
description	Recently, researchers are investing more fervently in fault diagnosis area of electrical machines. The users and manufacturers of these various efforts are strong to contain diagnostic features in software for improving reliability and scalability. Internet of Things (IoT) has grown immensely and contributing for the development of recent technological advancements in industries, medical and various environmental applications. It provides efficient processing power through cloud, and presents various new opportunities for industrial automation by implementing IoT and industrial wireless sensor networks. The process of regular monitoring enables early detection of machine faults and hence beneficial for Industrial automation by providing efficient process control. The performance of fault detection and its classification by implementing machine-learning algorithms highly dependent on the amount of features involved. The accuracy of classification will adversely affect by the dimensionality features increment. To address these problems, the proposed work presents the extraction of relevant features based on oriented sport vector machine (FO-SVM). The proposed algorithm is capable for extracting the most relevant feature set and hence presenting the accurate classification of faults accordingly. The extraction of most relevant features before the process of classification results in higher classification accuracy. Moreover it is observed that the lesser dimensionality of propose process consumes less time and more suitable for cloud. The experimental analysis based on the implementation of proposed approach provides and solution for the monitoring of machine condition and prediction of fault accurately based on cloud platform using industrial wireless sensor networks and IoT service.
format	article
author	Zhang Xiaoran Rane Kantilal Pitambar Kakaravada Ismail Shabaz Mohammad
author_facet	Zhang Xiaoran Rane Kantilal Pitambar Kakaravada Ismail Shabaz Mohammad
author_sort	Zhang Xiaoran
title	Research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology
title_short	Research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology
title_full	Research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology
title_fullStr	Research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology
title_full_unstemmed	Research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology
title_sort	research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/ab2863de282a49bba405ac432cb5c233
work_keys_str_mv	AT zhangxiaoran researchonvibrationmonitoringandfaultdiagnosisofrotatingmachinerybasedoninternetofthingstechnology AT ranekantilalpitambar researchonvibrationmonitoringandfaultdiagnosisofrotatingmachinerybasedoninternetofthingstechnology AT kakaravadaismail researchonvibrationmonitoringandfaultdiagnosisofrotatingmachinerybasedoninternetofthingstechnology AT shabazmohammad researchonvibrationmonitoringandfaultdiagnosisofrotatingmachinerybasedoninternetofthingstechnology
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Research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology

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