fault is an undesired and unexpected event that changes the system behaviour resulting in performance degradation or even instability, so how to detect and diagnose fault become a great deal in engineering community. ...
详细信息
fault is an undesired and unexpected event that changes the system behaviour resulting in performance degradation or even instability, so how to detect and diagnose fault become a great deal in engineering community. In this study, an adaptivefuzzywaveletnetwork-basedfaultdetection and diagnosis (AFWN-FDD) scheme is proposed for non-linear systems subject to unstructured uncertainty. The proposed scheme is composed of a diagnostic estimator and an adaptivefuzzywaveletnetwork (AFWN). Diagnostic estimator is designed for residual generation and faultdetection and AFWN based on multi-resolution analysis of wavelet transform and fuzzy concept is proposed to approximate the model of fault. Learning algorithm of the proposed AFWN-FDD scheme is derived in the Lyapunov stability sense. The proposed scheme can simultaneously detect and estimate multiple incipient and abrupt faults in the presence of uncertainty. Stability analysis for the presented faultdetection and diagnosis (FDD) scheme is provided. Furthermore, an extension of the proposed scheme for a class of non-linear systems with unmeasured states is presented. The efficiency and performance of the proposed scheme is evaluated through simulations that are performed for two well-known case studies. Comparison results highlight the superiority and capability of the proposed scheme.
暂无评论