集员辨识与T-S模型相结合的非线性系统建模及其故障检测算法

针对带有未知但有界（Unknown But Bounded．UBB）噪声的非线性系统的建模及其故障检测问题，提出了一种集员辨识与T-s模糊模型相结合的非线性系统建模及其故障检测算法。在建立非线性系统模型时，利用系统正常状态下的运行数据，选用T-S模型对其进行离线建模。首先采用模糊聚类的方法对输入空间进行模糊划分，然后利用T-S模型为参数线性模型的特点，使用参数线性集员辨识算法辨识T-S模型的结论参数。由于集员辨识算法所得到的是参数的集合估计，在系统运行过程中，可以很方便地利用所建模型预测实际系统的输出范围，如果测量所得实际系统的输出不在预测输出范围之内，则可判断系统发生了故障。通过与其他算法相比，验证了本方法的性能。

Nonlinear System Modeling and Fault Detection Algorithm Using Set Membership Identification and T-S Model

A modeling method is proposed and applied in fault detection for nonlinear systems with unknown but bounded noises.This method used a set membership identification algorithm to build Takagi-Sugeno(T-S) fuzzy models of nonlinear systems. After some input and output data of a system were obtained when it run without a fault,the input space was partitioned using a fuzzy clustering algorithm,and then the consequence parameters of the T-S fuzzy model of this system were estimated using a linear-in-parameter set membership identification algorithm.Since the result of the estimation was a set of parameters,it could be easily used to predict the interval of the actual system output.If the measured output was out of the predicted interval,it could be determined that a fault had occurred.Simulation experiments are performed,showing the performance of our method as compared to the other method.