LAUSR

In closed-loop feedback control systems, faults are propagated through the feedback link, which eventually leads to the abnormality of the entire system. Generally, it is very difficult to identify the faults of systems under the influence of the closed-loop feedback link. The existence of redundancy improves the reliability of the system. Meanwhile, it also poses new challenges to the fault diagnosis of multiple redundant systems. In this regard, a causality-based method is proposed for the fault diagnosis of closed-loop feedback control system with multiple modular redundancy. The dynamic Bayesian networks for fault diagnosis are established based on sensor data and system parameters. The networks consist of four layers, which are sensors, performances, monitors, and faults, respectively. Furthermore, the conditional probabilities of the fault nodes are calculated by Noisy-OR and Noisy-MAX models. The proposed method can dynamically evaluate system performance and integrate other monitoring information as evidence to assist faults diagnosis and location. A double modular redundant control system for a subsea blowout preventer is used as a case to demonstrate the proposed method, and the results show that the proposed method has high accuracy. The influence of sampling frequency, noise, and redundancy mode on diagnosis results is studied and discussed in the case study.