基于RBF神经网络的传感器非线性故障鲁棒诊断  

Robust Nonlinear Fault Diagnosis for Sensors Based on RBF Neural Network

作　　者：贾明兴[1] 王福利[1] 何大阔[1] 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110004

出　　处：《东北大学学报（自然科学版）》2004年第8期719-722,共4页Journal of Northeastern University(Natural Science)

基　　金：辽宁省自然科学基金资助项目(002013)

摘　　要：针对一类非线性系统,传感器非线性故障情形,提出了新的故障诊断方法·该方法采用状态变量扩展技术将传感器故障转化为系统故障进行诊断,RBF神经网络对传感器故障的导函数进行估计,网络权值在线调整,进而实现故障的实时估计·对于系统中存在的不确定性,故障诊断方法应用阈值处理技术,使算法具有一定鲁棒性·对于给出的算法,证明了Lyapunov稳定性·最后,给出了仿真实例,结果验证了该方法的正确性·A new method of nonlinear fault diagnosis for sensors is proposed for a class nonlinear system. Transforming sensors faults into systematic ones via extended state variables with the fault derivative function estimated by RBF neural network, the method can adjust on-line the weights of network to implement real-time estimate of fault values. With the threshold processing technique applied to fault diagnosis for the uncertainties in the system, robustness is provided to the algorithm to a certain degree. The Lyapunov stability of the proposed method is proved and its correctness verified through a simulation as instance.

关 键 词：故障诊断 传感器 非线性 神经网络 鲁棒性 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]