Fault Diagnosis and Application to Modern Systems

Journal of Control Science and Engineering, May 2017

Xiao He, Zidong Wang, Gang Li, Zhijie Zhou, Youqing Wang

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Fault Diagnosis and Application to Modern Systems

Fault Diagnosis and Application to Modern Systems Xiao He,1 Zidong Wang,2 Gang Li,3 Zhijie Zhou,4 and Youqing Wang5 1Department of Automation, TNList, Tsinghua University, Beijing 100084, China 2Department of Computer Science, Brunel University London, London UB8 3PH, UK 3School of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China 4Xi’an High-Tech Institute, Xi’an 710025, China 5College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China Correspondence should be addressed to Xiao He; nc.ude.auhgnist@oaixeh Received 27 April 2017; Accepted 27 April 2017; Published 24 May 2017 Copyright © 2017 Xiao He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. With the increasing demand for higher performance and higher safety and reliability standards, fault diagnosis (FD) for modern control systems has become an active field of research over the past decades. FD is a theory and technology that can utilize the analytical redundancy relationship of the control system and provide the whole system with accurate information of the fault that occurred by using the input and output of the system. Both theoretical challenge and practical application demands of many kinds of real-time control systems motivate the investigation of FD. A typical FD process consists of a fault detection unit, a fault isolation strategy, and a fault estimation technique. The task of fault detection problem is to construct a residual signal which is then compared with a predefined threshold. When the residual exceeds the threshold, an alarm is generated. The scope of the fault isolation problem is to locate the true fault from all possible faults. The objective of the fault estimation problem is to determine the fault amplitude as well as the emergence time of the fault. The precise understanding of the fault that occurred by using FD technology is a prerequisite for the next fault accommodation process and thus is very important for system safety. The existing FD techniques can be generally divided into two main categories: the model-based method and the data-driven approach. In the case where a mathematical model can be obtained for an objective system, a model-based FD method can provide exact decoupling or maximum attenuation to the factors except the target fault. On the other hand, when it is hard to obtain a mathematical model but enough historical data of a system can be obtained, the data-driven approach is more applicable and may get a better FD result. Recently, analytical-based FD techniques for modern systems have received more and more attention and there have been an increasing number of results reported in the literature for the topics of FD. This special issue aims to bring together some typical recent developments in fault diagnosis technology and its application to modern systems. We have solicited submissions to this special issue from scholars from all over the world and have got 26 submissions. After rigorous peer-review processes, 9 papers have been selected to be published in this special issue. Some works in this special issue focus on the model-based FD and Fault-Tolerant Control (FTC) techniques. In the paper entitled “Detection of Intermittent Fault for Discrete-Time Systems with Output Dead-Zone: A Variant Tobit Kalman Filtering Approach” by J. Huang and X. He, a fault detection method for discrete-time systems with output dead-zone is proposed. Most of the present research on fault diagnosis focuses on input dead-zone instead of output dead-zone, where the dead-zone is usually treated as an unknown input. The Tobit Kalman filtering approach is employed in the design of the residual generator. Compared with the traditional Kalman filter, the Tobit Kalman filter avoids the estimation bias that is brought about by ignoring the correlation between the state and the measurement noise in the dead-zone. Due to the obvious discontinuity occurring in thresholds of the dead-zone, the gradient does not exist, which makes the Extended Kalman filter hard to apply. Also, when the output dead-zone is located between the sigma points, there will be a bias in the measurement noise covariance that causes a bias in the estimation using Unscented Kalman filter. By contrast, the above conditions can be averted by utilizing the Tobit Kalman filter that provides the optimal estimation with minimum variance when the output dead-zone appears. Even though the particle filter can give an accurate estimation in the output dead-zone, the Tobit Kalman filter has similar computational expenses to the traditional Kalman filter to avoid the collapse that may well happen in applying a particle filter in the high dimensional systems. Therefore, the fault detection metho (...truncated)


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Xiao He, Zidong Wang, Gang Li, Zhijie Zhou, Youqing Wang. Fault Diagnosis and Application to Modern Systems, Journal of Control Science and Engineering, 2017, 2017, DOI: 10.1155/2017/1026759