Intelligent isolation switch mechanical failure recognition system based on multisource information fusion

To address the high mechanical failure rate of isolation switches, complicated manual recognition processes, and low accuracy, an intelligent isolation switch mechanical failure recognition system based on multi-source information fusion is proposed. The types of isolation switches and the main failure characteristics are analyzed, and the correspondence between the types of failures and the state quantities is explained. The state monitoring variables are proposed, and the appropriate data analysis model is selected based on the state monitoring variables. Data training and result analysis are performed using pattern recognition methods and BP neural networks. A multi-source fusion-based isolation switch state detection platform is proposed, and the overall architecture, software architecture, and hardware architecture are established. The effect of isolation switch state recognition in the application of the system in specific engineering is analyzed, and the validity of the proposed failure recognition system is explained.