A Novel Online Active Fault Diagnosis Method Based on Invariant Sets
Songlin Yang, Feng Xu, Xueqian Wang, Bin Liang
Abstract
This letter proposes a novel online active fault diagnosis (AFD) method based on invariant sets for linear time-invariant (LTI) systems with bounded disturbances and noises. In general, the system has the healthy mode and several faulty modes. During system operation, with respect to the healthy and faulty modes, the corresponding healthy and faulty output estimation sets are computed, respectively. Thus, the task of AFD can be converted to separate the healthy and faulty output estimation sets, which can consequently distinguish the current system mode and implement AFD. The existing AFD methods implement the objective by designing an input sequence to separate the healthy and faulty output estimation sets. However, it is difficult to use these methods to simultaneously combine AFD and fault-tolerant control. Thus, we propose a new AFD method in this letter to online design inputs step by step, which has potential to overcome some weakness of the existing AFD methods. Particularly, we implement this AFD objective by online designing inputs at each step to maximize the distances of the healthy and faulty output estimation sets. Moreover, this online input design problem can finally be transformed into the resolution of two mixed integer programming (MIP) problems with a similar structure. At the end, a quadruple-tank benchmark system is used to illustrate the effectiveness of the proposed method by comparing with an existing set-based AFD method.