Altaf, SaudAl-Anbuky, AdnanGheitasi, Alireza2025-02-092025-02-092024-11-26Altaf, S., Al-Anbuky, A., & Gheitasi, A. (2024). Enhancing Fault Detection in Distributed Motor Systems Using AI-Driven Cyber-Physical Sensor Networks. Engineering Proceedings, 82(1), 78. https://doi.org/10.3390/ecsa-11-204692673-4591http://hdl.handle.net/10292/18623Defect detection in distributed motors within the IoED architecture is the focus of this research. The idea of the distributed Internet of Things (DIoT) is used to build a cyber-physical system architecture. To improve sensitivity and accuracy, this approach uses fast Fourier transform (FFT) for signal processing and an ANN for defect detection. When it comes to motor conditions, ANNs can adapt to different situations and find complicated patterns, whereas FFT is good at extracting frequency characteristics. The experimental results confirm the system’s usefulness in various failure scenarios, highlighting its resilience and capacity to detect faults in real time. This enhances the predictability of manufacturing motor systems.© 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/4.0/4605 Data Management and Data Science4606 Distributed Computing and Systems Software46 Information and Computing Sciences40 EngineeringConference ContributionOpenAccess10.3390/ecsa-11-20469