Modeling of Failure Mechanisms and Intelligent Prediction Methods for Automatic Transfer Switches

This paper systematically investigates the fault mechanism modeling and intelligent prediction methods for Automatic transfer switching equipment (ATSE). First, an in-depth analysis of common ATSE failure types and their underlying mechanisms is conducted, covering mechanical faults, electrical faults, and control logic faults. On this basis, a fault mechanism model based on electro-thermal-stress multiphysics coupling is established, comprehensively considering the interactive effects of electromagnetic, thermal, and stress fields on ATSE performance. Furthermore, an intelligent fault prediction method based on a CNN-LSTM hybrid deep learning architecture is proposed, enabling real-time monitoring of ATSE operating conditions and early fault warning. Experimental and field validation results demonstrate that the established fault mechanism model accurately reflects the performance degradation trajectory of ATSE under various operating conditions, while the intelligent prediction model achieves over 95% accuracy in identifying both mechanical and electrical faults, showing significant improvement compared to traditional methods.