Intelligent Fault Diagnosis and Resilient Control of Ship Lock Electrical Systems Based on Multi-Sensor Fusion and Digital Twins

In this paper, we propose an intelligent diagnosis method based on multi-sensor fusion and digital twin collaboration for the fault diagnosis and control of the electrical system of the lock room under complex working conditions. We constructed a lightweight deep learning model to achieve accurate identification and localisation of multiple types of faults, and designed a three-stage resilient control strategy to ensure reliable operation of the system under fault conditions. Experimental results demonstrate that the method significantly improves the diagnostic accuracy and system reliability under harsh environments, shortens the fault response time, and improves the maintenance efficiency. In addition, the method is highly adaptable to different operating conditions and ensures sustained performance even in extreme environments. This work provides a new approach to the intelligent operation and maintenance of shipping equipment, helping to improve the safety and efficiency of maritime transport.