Al-Anbuky, AdnanAltaf, SaudGheitasi, Alireza2024-08-192024-08-192024-08-02Sensors (Basel), ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI AG, 24(15), 5012-. doi: 10.3390/s241550121424-82201424-8220http://hdl.handle.net/10292/17904This 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.© 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/).https://creativecommons.org/licenses/by/4.0/artificial neural networkcyber–physical systemdistributed Internet of thingsfast Fourier transformmotor fault detectionartificial neural networkcyber–physical systemdistributed Internet of thingsfast Fourier transformmotor fault detection40 Engineering4008 Electrical Engineering0301 Analytical Chemistry0502 Environmental Science and Management0602 Ecology0805 Distributed Computing0906 Electrical and Electronic EngineeringAnalytical Chemistry3103 Ecology4008 Electrical engineering4009 Electronics, sensors and digital hardware4104 Environmental management4606 Distributed computing and systems softwareJournal ArticleOpenAccess10.3390/s24155012