Fault Detection and Prevention Mechanism Driven by Sensors in Electrical Automation Systems

This article analyzes the various factors that cause faults in electrical automation equipment in complex working environments and long-term operation. Based on these analyses, ANN is used to monitor and analyze the data collected by equipment sensors, in order to identify and predict potential faults. The study also proposes a series of preventive strategies and data-driven preventive maintenance to help prevent failures, improve equipment reliability and service life. Experimental data shows that the accuracy of fault detection using Artificial Neural Network (ANN) is significantly higher than that of traditional k-Nearest Neighbors (KNN), with an average accuracy of 88.155%, while KNN is only 72.08%. Especially in the best case, the accuracy of the ANN model is as high as 97.4%, demonstrating its advantage in identifying fault features in complex systems. By implementing sensor driven fault detection and prevention mechanisms, this article can not only improve the reliability and safety of electrical automation systems, but also enhance overall production efficiency and economic benefits.