Active Fault Diagnosis for Linear Systems: Within a Signal Processing Framework

Compared with passive fault diagnosis (PFD) techniques, the research on active fault diagnosis (AFD) techniques is insufficient. The existing AFD methods generally include set-based method, Bayesian approach, and so on. Few AFD results within the framework of signal processing have been published. In this article, a new AFD framework, which is composed of auxiliary input generation, fault diagnosis observer, and signal analysis unit, is introduced into AFD analysis for the first time, within the signal processing framework. Auxiliary signal generation unit is used to generate appropriate auxiliary input signal according to the requirements of fault diagnosis. Then, a set of fault diagnosis observers are designed to generate residuals corresponding to the input channels one to one. Two signal processing methods, correlation analysis and ESPRIT algorithm, are applied to extract the relationship between auxiliary signal and residual signal for fault diagnosis. Numerical examples are given to illustrate the effectiveness of the proposed method.