This is the second-part paper of the survey on fault diagnosis and fault-tolerant techniques, where fault diagnosis methods and applications are overviewed respectively from the knowledge-based and hybrid/active viewpoints. With the aid of the first-part survey paper, the second-part review paper completes a whole overview on the fault diagnosis techniques and their applications. Comments on advantages and constraints of various diagnosis techniques, including model-based, signalbased, knowledge-based, and hybrid/active diagnosis techniques, are also given. An overlook on the future development of the fault diagnosis is presented.
His main research interests include robust adaptive signal processing, state estimation and its applications to condition monitoring of aero engines and wind generators, wireless communication, wireless sensor networks and wireless networked control systems. Zhiwei Gao (SM'08) received the B.Eng. degree in Electric Engineering and Automation, and the M.Eng. and
-Robust fault estimation plays an important role in real-time monitoring, diagnosis and fault-tolerance control. Accordingly, this paper aims to develop an effective fault estimation technique to simultaneously estimate the system states and the concerned faults, while minimizing the influences from process/sensor disturbances. Specifically, an augmented system is constructed by forming an augmented state vector composed of the system states and the concerned faults. Next, an unknown input observer is designed for the augmented system by decoupling the partial disturbances and attenuating the disturbances that cannot be decoupled, leading to a simultaneous estimate of the system states and the concerned faults. In order to be close to the practical engineering situations, the process disturbances in this study are assumed not to be completely decoupled. In the first part of the paper, the existence condition of such an unknown input observer is proposed to facilitate the fault estimation for linear systems subjected to process disturbances. In the second part, robust fault estimation techniques are addressed for Lipschitz nonlinear systems subjected to both process and sensor disturbances. The proposed technique is finally illustrated by the simulation studies of a three-shaft gas turbine engine and a single-link flexible joint robot.
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