基于模糊奇偶方程的非线性系统传感器故障诊断

 给出了一种非线性系统传感器的故障诊断方法。该方法将T-S 模糊模型、全解耦奇偶方程和参数估计相结合,同时对非线性系统的多个传感器的故障进行检测、隔离与识别。设计出用于产生残差的线性系统全解耦奇偶方程,并给出了全解耦奇偶向量的存在条件,全解耦奇偶方程产生的残差仅对一个传感器故障敏感,而对系统状态、扰动输入和其它传感器输出解耦。引入T-S 模型将全解耦奇偶方程推广到非线性系统中得到了模糊奇偶方程。传感器的故障模型表示为刻度因子和偏差的形式,根据残差信息应用卡尔曼估计方法可识别出故障模型的参数。最后给出了某型号飞机控制系统传感器的故障诊断仿真实例。

Sensor Fault Diagnosis Approach for Nonlinear Systems Based on Fuzzy P arity Equation

A new approach to the sensor fault diagnosis of nonlinear systems is presented. In this method, the Takagi Sugeno (T S) fuzzy model, fully decoupled parity equations and parameter estimation are combined to detect, isolate and identify sensor faults for nonlinear systems. The fully decoupled parity equations in linear systems are designed to generate residuals. And, the existence condition of the fully decoupled parity vector is given. The residual which is sensitive to a special sensor fault is decoupled from system states, disturbance inputs and other sensor faults. The T S model is used to extend the fully decoupled parity equations to fuzzy parity equations for nonlinear systems. The sensor faults are represented as biases and scale factor changes. The biases and changes in the scale factor can be identified using the information contained in the residuals by a parameter estimator based on the Kalman filter. A simulation example of a nonlinear aircraft control system is given for illustration.