基于RBF神经网络的非线性系统故障诊断

Fault diagnosis based on RBF neural network in a class of nonlinear systems

针对一类含模型不确定性的非线性系统,提出了具有强鲁棒性和高灵敏度的在线故障检测与诊断方法.其中,系统只有输入、输出可检测,故障是关于输入和状态的非线性函数.非线性在线估计器用于估计系统不确定部分,同时监视系统是否发生故障,估计故障的大小.仿真结果表明,故障诊断算法稳定.

A new online fault detection and diagnosis method which processes powerful robustness and high sensitivity for a class of nonlinear system with model uncertainty is proposed. The only measurable variables are the inputs and outputs of the system. The faults are assumed to be functions of the inputs and the states of the system. A nonlinear online approximator is utilized to estimate uncertain parts in the system, simultaneously, to monitor the faults and estimate the fault value. The diagnosis algorithm is stable. Finally, a simulation example is presented to illustrate the effectiveness of the method.