Fault Detection Method Based on Machine Learning in Ship Lock Automation Control

This paper proposes a remote fault diagnosis mode for ship lock electromechanical systems based on machine learning theory to address the problems of labor-intensive, inefficient, and untimely response that exist in the traditional electromechanical operation and maintenance methods of ship locks. By utilizing historical operational data of ship lock electromechanical equipment and training machine learning models with real-time equipment status data as input, automatic analysis of fault symptoms is achieved, enabling early warning of faults, automatic determination of fault types, rapid identification of fault roots, and providing probability analysis of suspicious items. This provides guidance for maintenance personnel's repair work and offers a new approach for intelligent operation and maintenance of ship lock electromechanical equipment.