A unified sensor and actuator fault diagnosis in digital twins for remote operations

Abstract

This paper explores the development of a unified hybrid approach for sensor and actuator fault diagnosis in digital twins for remote operations. Central to this approach is the implementation of a robust adaptive Kalman filter algorithm, which forms the backbone of the proposed unified algorithm. The essence of this unified algorithm lies in its capability to effectively filter the sensor measurements. The algorithm is enriched with tuning parameters, offering flexibility in adjusting the convergence rate to suit operational requirements. Noteworthy for its robustness, our approach excels in handling uncertainties and diverse types of faults, including drift, bias, noise, and freeze fault scenarios. Through comprehensive simulation and experimental evaluations conducted on a small surface vessel, the method demonstrates remarkable proficiency in accurately identifying sensor and actuator faults. This precision enables early detection and prompt mitigation of anomalies, contributing to heightened operational resilience.