An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information...

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Autores principales:	Ayaz Kafeel, Sumair Aziz, Muhammad Awais, Muhammad Attique Khan, Kamran Afaq, Sahar Ahmed Idris, Hammam Alshazly, Samih M. Mostafa
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	signal analysis empirical mode decomposition artificial intelligence machine faults supervised learning support vector machines Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/b1d2b0eee736430da2f65795e7bd1d8e
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spelling	oai:doaj.org-article:b1d2b0eee736430da2f65795e7bd1d8e2021-11-25T18:57:39ZAn Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis10.3390/s212275871424-8220https://doaj.org/article/b1d2b0eee736430da2f65795e7bd1d8e2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7587https://doaj.org/toc/1424-8220Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine’s health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate.Ayaz KafeelSumair AzizMuhammad AwaisMuhammad Attique KhanKamran AfaqSahar Ahmed IdrisHammam AlshazlySamih M. MostafaMDPI AGarticlesignal analysisempirical mode decompositionartificial intelligencemachine faultssupervised learningsupport vector machinesChemical technologyTP1-1185ENSensors, Vol 21, Iss 7587, p 7587 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	signal analysis empirical mode decomposition artificial intelligence machine faults supervised learning support vector machines Chemical technology TP1-1185
spellingShingle	signal analysis empirical mode decomposition artificial intelligence machine faults supervised learning support vector machines Chemical technology TP1-1185 Ayaz Kafeel Sumair Aziz Muhammad Awais Muhammad Attique Khan Kamran Afaq Sahar Ahmed Idris Hammam Alshazly Samih M. Mostafa An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis
description	Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine’s health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate.
format	article
author	Ayaz Kafeel Sumair Aziz Muhammad Awais Muhammad Attique Khan Kamran Afaq Sahar Ahmed Idris Hammam Alshazly Samih M. Mostafa
author_facet	Ayaz Kafeel Sumair Aziz Muhammad Awais Muhammad Attique Khan Kamran Afaq Sahar Ahmed Idris Hammam Alshazly Samih M. Mostafa
author_sort	Ayaz Kafeel
title	An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis
title_short	An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis
title_full	An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis
title_fullStr	An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis
title_full_unstemmed	An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis
title_sort	expert system for rotating machine fault detection using vibration signal analysis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b1d2b0eee736430da2f65795e7bd1d8e
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An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

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