Adaptive fuzzy wavelet network for robust fault detection and diagnosis in non-linear systems

• Published in: IET control theory & applications Vol. 8; no. 15; pp. 1487 - 1498
• Main Authors: Shahriari-kahkeshi, Maryam, Sheikholeslam, Farid
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 16.10.2014
• Subjects: adaptive control; adaptive fuzzy wavelet network‐based fault detection and diagnosis scheme; AFWN‐FDD scheme; diagnostic estimator; Dynamical systems; engineering community; Fault detection; Fault diagnosis; Fuzzy; Fuzzy logic; Fuzzy set theory; learning algorithm; learning systems; Lyapunov methods; Lyapunov stability sense; multiresolution analysis; Networks; nonlinear control systems; Nonlinear dynamics; nonlinear systems; performance degradation; robust control; robust fault detection; robust fault diagnosis; stability; stability analysis; unstructured uncertainty; wavelet transform; wavelet transforms; AFWN-FDD scheme; learning algorithm; fault diagnosis; wavelet transforms; robust fault diagnosis; fuzzy set theory; stability analysis; wavelet transform; adaptive control; performance degradation; learning systems; nonlinear systems; engineering community; Lyapunov stability sense; nonlinear control systems; robust fault detection; robust control; adaptive fuzzy wavelet network-based fault detection and diagnosis scheme; unstructured uncertainty; multiresolution analysis; diagnostic estimator; stability; Lyapunov methods
• ISSN: 1751-8644; 1751-8652
• DOI: 10.1049/iet-cta.2013.0960

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 adaptive fuzzy wavelet network-based fault detection 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 adaptive fuzzy wavelet network (AFWN). Diagnostic estimator is designed for residual generation and fault detection 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 fault detection 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.