一类非线性系统的故障诊断方法

Fault Diagnosis of a Nonlinear System

针对一类非线性系统,提出了一种新的故障诊断方法;首先,对未知的系统正常模式和系统故障模式分别进行确定学习,将系统正常模式和各种故障模式以空间分布的常数神经网络权值方式储存,建立模式库;然后,根据已有模式库中的模式构造一系列估计器,将估计器的状态与实际系统状态进行比较,构造残差,以此来检测和分离各种故障;最后,以弹簧减震器系统为例,用仿真结果证明了文中设计的故障诊断方法的可行性和有效性。

In this paper,a fault diagnosis scheme is proposed for a class of nonlinear system.Firstly,the uncertainty system and unknown fault dynamics are identified through deterministic learning.The knowledge on the fault dynamics is stored in a space distribution bank of invariable neural networks.Secondly,based on the bank which has been established,a series of estimators are constructed to compare with the test monitored system and a set of residual can be generated.So,the fault can be detected and isolated by the residual.At last,simulation studies of mass-spring-damper system are included to demonstrate the effectiveness of the proposed approach.