{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:35:11Z","timestamp":1771468511747,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T00:00:00Z","timestamp":1722556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AUCOM Ltd."},{"name":"Callaghan NZ"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This 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\u2013physical system architecture and mathematical modeling framework for efficient fault detection. The mathematical model is designed to capture the intricate relationships within the cyber\u2013physical 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\u2019s performance is evaluated in terms of accuracy, sensitivity, and false positive rates.<\/jats:p>","DOI":"10.3390\/s24155012","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T16:14:58Z","timestamp":1722615298000},"page":"5012","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Cyber\u2013Physical Distributed Intelligent Motor Fault Detection"],"prefix":"10.3390","volume":"24","author":[{"given":"Adnan","family":"Al-Anbuky","sequence":"first","affiliation":[{"name":"Sensor Network and Smart Environment Research Centre (SeNSe), Auckland University of Technology, Auckland 1010, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3455-6854","authenticated-orcid":false,"given":"Saud","family":"Altaf","sequence":"additional","affiliation":[{"name":"Sensor Network and Smart Environment Research Centre (SeNSe), Auckland University of Technology, Auckland 1010, New Zealand"}]},{"given":"Alireza","family":"Gheitasi","sequence":"additional","affiliation":[{"name":"Sensor Network and Smart Environment Research Centre (SeNSe), Auckland University of Technology, Auckland 1010, New Zealand"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1109\/ACCESS.2021.3136573","article-title":"An improved motion control with cyber-physical uncertainty tolerance for distributed drive electric vehicle","volume":"10","author":"Cao","year":"2022","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5565","DOI":"10.1109\/TVT.2021.3076105","article-title":"A distributed integrated control architecture of AFS and DYC based on MAS for distributed drive electric vehicles","volume":"70","author":"Liang","year":"2021","journal-title":"IEEE Trans. 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