Subjects: Computer Science >> Computer Software submitted time 2024-01-07
Abstract: This thesis proposes a method for industrial process control loop fault diagnosis based on graph neural networks. By monitoring the output signals of loop sensors, the graph neural network can capture abnormal behaviors in the loop and automatically diagnose the type of loop faults. Experimental results demonstrate that the proposed method can efficiently detect loop faults and achieve high accuracy in both single and multiple fault scenarios. This method provides a reliable fault diagnosis solution for industrial process control, which has important practical significance and application value in actual industrial applications.
Peer Review Status:Awaiting Review