Novel fault diagnosis for discrete time-varying system based on angle correction iterative learning
基于角度修正迭代学习的离散时变系统故障诊断

A novel fault detection and estimation algorithm is proposed to solve the fault diagnosis problem for a class of nonlinear discrete time-varying system. By using the residual signal, the algorithm adjusts the introduced virtual fault through an iterative learning procedure in the selected optimization time-domain. To speed up the algorithm convergence, the angle relationship between the actual output and the output in the vector space of the fault tracking estimator is employed to modify the iterative learning law for the virtual fault. The proposed algorithm not only detects and estimates differenttypes of faults but also makes full use of the update information of the estimator output signal to effectively improve the algorithm convergent rate of the algorithm. Simulation results verify the validity of the algorithm.