A Fault Diagnosability Evaluation Method for Dynamic Systems Without Distribution Knowledge

This article addresses the quantitative fault diagnosability evaluation for dynamic systems without distribution knowledge. The system dynamics in different cases are first characterized by mean vectors and covariance matrices. Then, fault detectability and isolability are defined based on the constructed characteristics. On this basis, the Mahalanobis distance (MD) is employed to propose a fault diagnosability analysis measure. Furthermore, model-based and data-driven algorithms for fault diagnosability evaluation are given according to the MD-based measure. In addition, the reliabilities of the evaluation results are considered by taking advantage of ambiguity sets of mean vectors and covariance matrices of the system dynamics. Finally, two examples are employed to verify the effectiveness of the proposed algorithms.