Fault detection for nonlinear discrete-time systems via deterministic learning  

作　　者：Junmin HU 

机构地区：[1]School of Automation Science and Engineering, South China University of Technology, Guangzhou Guangdong 510640, China [2]Guangdong Key Laboratory of Biomedical Engineering, Guangzhou Guangdong 510640, China

出　　处：《Control Theory and Technology》2016年第2期159-175,共17页控制理论与技术（英文版）

基　　金：This work was supported by the National Science Fund for Distinguished Young Scholars （No. 61225014）, the National Major Scientific Instruments Development Project （No. 61527811）, the National Natural Science Foundation of China （Nos. 61304084, 61374119）, the Guangdong Natural Science Foundation （No. 2014A030312005）, and the Space Intelligent Control Key Laboratory of Science and Technology for National Defense.

摘　　要：Recently, an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems. In this paper, a fault detection scheme is proposed for a class of nonlinear discrete-time systems via deterministic learning. By using a discrete-time extension of deterministic learning algorithm, the general fault functions （i.e., the internal dynamics） underlying normal and fault modes of nonlinear discrete-time systems are locally-accurately approximated by discrete-time dynamical radial basis function （RBF） networks. Then, a bank of estimators with the obtained knowledge of system dynamics embedded is constructed, and a set of residuals are obtained and used to measure the differences between the dynamics of the monitored system and the dynamics of the trained systems. A fault detection decision scheme is presented according to the smallest residual principle, i.e., the occurrence of a fault can be detected in a discrete-time setting by comparing the magnitude of residuals. The fault detectability analysis is carried out and the upper bound of detection time is derived. A simulation example is given to illustrate the effectiveness of the proposed scheme.Recently, an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems. In this paper, a fault detection scheme is proposed for a class of nonlinear discrete-time systems via deterministic learning. By using a discrete-time extension of deterministic learning algorithm, the general fault functions （i.e., the internal dynamics） underlying normal and fault modes of nonlinear discrete-time systems are locally-accurately approximated by discrete-time dynamical radial basis function （RBF） networks. Then, a bank of estimators with the obtained knowledge of system dynamics embedded is constructed, and a set of residuals are obtained and used to measure the differences between the dynamics of the monitored system and the dynamics of the trained systems. A fault detection decision scheme is presented according to the smallest residual principle, i.e., the occurrence of a fault can be detected in a discrete-time setting by comparing the magnitude of residuals. The fault detectability analysis is carried out and the upper bound of detection time is derived. A simulation example is given to illustrate the effectiveness of the proposed scheme.

关 键 词：Fault detection nonlinear discrete-time systems deterministic learning neural networks locally accurate modeling 

分 类 号：TP271.8[自动化与计算机技术—检测技术与自动化装置] TP18[自动化与计算机技术—控制科学与工程]