Fault detection for nonlinear discrete-time systems via deterministic learning
Junmin HU
Cong WANG
Xunde DONG
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.
机标关键词：fault detectionradial basis functionlearning algorithmthe differencessystem dynamicsrapid detectionlearning theorydetection time
资助基金：the National Science Fund for Distinguished Young Scholars(61225014)the National Major Scientific Instruments Development Project(61527811)the National Natural Science Foundation of China()the National Natural Science Foundation of China(61304084)the National Natural Science Foundation of China(61374119)the Guangdong Natural Science Foundation(2014A030312005)
论文发表日期：2016-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：17( 159-175 )
英文信息