| 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. |
| 关键词: Fault detection, nonlinear discrete-time systems, deterministic learning, neural networks, locally accurate modeling |
| DOI： |
| Received:October 21, 2014Revised:March 25, 2016 |
| 基金项目: |
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| Fault detection for nonlinear discrete-time systems via deterministic learning |
| J. Hu,C. Wang,X. Dong |
| (School of Automation Science and Engineering, South China University of Technology; Guangdong Key Laboratory of Biomedical Engineering;School of Automation Science and Engineering, South China University of Technology, Guangzhou Guangdong 510640, China; Guangdong Key Laboratory of Biomedical Engineering) |
| Abstract: |
| 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. |
| Key words: Fault detection, nonlinear discrete-time systems, deterministic learning, neural networks, locally accurate modeling |
