Cyber-Physical Distributed Intelligent Motor Fault Detection

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aut.relation.issue15
aut.relation.journalSensors (Basel)
aut.relation.startpage5012
aut.relation.volume24
dc.contributor.authorAl-Anbuky, Adnan
dc.contributor.authorAltaf, Saud
dc.contributor.authorGheitasi, Alireza
dc.date.accessioned2024-08-19T00:58:33Z
dc.date.available2024-08-19T00:58:33Z
dc.date.issued2024-08-02
dc.description.abstractThis research paper explores the realm of fault detection in distributed motors through the vision of the Internet of electrical drives. This paper aims at employing artificial neural networks supported by the data collected by the Internet of distributed devices. Cross-verification of results offers reliable diagnosis of industrial motor faults. The proposed methodology involves the development of a cyber-physical system architecture and mathematical modeling framework for efficient fault detection. The mathematical model is designed to capture the intricate relationships within the cyber-physical system, incorporating the dynamic interactions between distributed motors and their edge controllers. Fast Fourier transform is employed for signal processing, enabling the extraction of meaningful frequency features that serve as indicators of potential faults. The artificial neural network based fault detection system is integrated with the solution, utilizing its ability to learn complex patterns and adapt to varying motor conditions. The effectiveness of the proposed framework and model is demonstrated through experimental results. The experimental setup involves diverse fault scenarios, and the system's performance is evaluated in terms of accuracy, sensitivity, and false positive rates.
dc.identifier.citationSensors (Basel), ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI AG, 24(15), 5012-. doi: 10.3390/s24155012
dc.identifier.doi10.3390/s24155012
dc.identifier.issn1424-8220
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10292/17904
dc.languageeng
dc.publisherMDPI AG
dc.relation.urihttps://www.mdpi.com/1424-8220/24/15/5012
dc.rights© 2024 by the authors. 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/).
dc.rights.accessrightsOpenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectartificial neural network
dc.subjectcyber–physical system
dc.subjectdistributed Internet of things
dc.subjectfast Fourier transform
dc.subjectmotor fault detection
dc.subjectartificial neural network
dc.subjectcyber–physical system
dc.subjectdistributed Internet of things
dc.subjectfast Fourier transform
dc.subjectmotor fault detection
dc.subject40 Engineering
dc.subject4008 Electrical Engineering
dc.subject0301 Analytical Chemistry
dc.subject0502 Environmental Science and Management
dc.subject0602 Ecology
dc.subject0805 Distributed Computing
dc.subject0906 Electrical and Electronic Engineering
dc.subjectAnalytical Chemistry
dc.subject3103 Ecology
dc.subject4008 Electrical engineering
dc.subject4009 Electronics, sensors and digital hardware
dc.subject4104 Environmental management
dc.subject4606 Distributed computing and systems software
dc.titleCyber-Physical Distributed Intelligent Motor Fault Detection
dc.typeJournal Article
pubs.elements-id566444
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