基于最小二乘估计的非线性离散系统鲁棒故障诊断

对于一类不确定非线性离散系统，提出了一种基于最小二乘估计的故障诊断（FDBLSE）方法。该方法通过构造被诊断 状态估计器而得到状态估计误差与故障之间的动态关系，并以此为基础，利用最小二乘直接对故障进行在线辨识。文中分析了故障辨识误差以及诊断的鲁棒性、灵敏度和检测时间，并与基于学习的故障诊断方法作了深入比较。FDBL方法不但能够辨识故障，而且能够给出辨识误差的上界，同时还具有辨识时间短、辨识精度高的特点，仿真表明该方法有效。

Robust Fault Diagnosis for Nonlinear Systems Based on Least Squares Estimation

A fault diagnosis approach based on least squares estimation(FDBLSE) for a class of nonlinear systems is proposed in this paper. It first constructs a state estimator for the systems to be diagnosed so that the dynamic relationship between state estimation error and faults is obtained, it then identifies the faults using least squares estimation method based on the obtained dynamic relationship. The fault identification error and the robustness, sensitivity to faults and the detection time of the fault diagnosis are analyzed. A deep-going comparison between the approach proposed in this paper and the fault diagnosis approach based on the learning method is also given. FDBLSE approach can not only identify faults, but also give the upper limit of the identification error. It highlights short identification-time and high accuracy of fault identification. The simulation indicates the effectiveness of the approach proposed in this paper.