A Novel Control-Performance-Oriented Data-Driven Fault Classification Approach

This paper selected nine novel feature quantities that can reflect control performance in the closed-loop. Combined with support vector machine (SVM) and k-nearest neighbor (KNN), they are used to perform accurate fault diagnosis. Stability margin describes the degree of stability of the transfer function matrix of system, and thus can be used as an indicator to reflect system’s control performance. Similarly to stability margin, the reciprocals of $\boldsymbol {H_{\infty }}$-norms of eight subsystems related to stability margin can also be selected as the other eight features describing the control performance. To get the data-driven realization of nine feature quantities, stable image representation and stable kernel representation are constructed by data-driven method without system identification in closed-loop system. Through trained SVM and KNN, different types of faults can be accurately categorized by these feature quantities and the correctness of the entire algorithm is testified and demonstrated by a dc motor benchmark system.