Enhancing Fault Detection in Distributed Motor Systems Using AI-Driven Cyber-Physical Sensor Networks

Abstract

Defect 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.