Multiple faults diagnosis approach for nonlinear system
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摘要: 给出了非线性系统的一种基于模糊奇偶方程的多故障诊断方法.解决了非线性系统中同时出现多种故障时的故障检测与识别问题.首先构造线性系统的全解耦奇偶方程,再应用T-S模型融合非线性系统各个工作点处的线性模型的全解耦奇偶方程得到模糊奇偶方程.模糊奇偶方程产生的残差仅对一个执行器故障敏感、对一个传感器不敏感,而对其他执行器不敏感、对其他传感器敏感.将传感器和执行器故障模型表示成偏差的形式,根据残差信息可以估计出故障的模型参数.给出了应用递推最小二乘方法对各故障模型的参数进行估计的方法.给出了铁路牵引控制系统的感应电机仿真实例.结果表明,新方法能够对传感器故障和执行器故障同时存在的多故障进行诊断.Abstract: A new method for multiple faults diagnosis of nonlinear systems based on fuzzy parity equation was presented. This approach can be used to detect and identify the fault models when there are several kinds fault such as actuator fault and sensor fault simultaneously in the system. Fully decoupled parity equation for multiple faults was constructed, and fuzzy parity equation was obtained by using the T-S fuzzy model. Residual generated by the fuzzy parity equation is sensitive to one special actuator fault and all sensor faults except one special sensor, and insensitive to other actuators and the special sensor. The faults in sensors and actuators were represented as biases. The fault parameter can be identified from the information contained in the residuals. A parameter estimator based on recursive least square was designed. A simulation example on an induction motor of a railway traction system was given for illustration. Results show that the new approach can be used to detect and identify the faults of multiple sensors and multiple actuators in nonlinear systems.
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Key words:
- fault diagnosis /
- fuzzy sets /
- least square approximations /
- nonlinear systems /
- multiple faults /
- parity equation
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