摘要
针对传感器故障 ,提出了一种基于 RBF神经网络的集成故障诊断方法。用 RBF神经网络建立传感器故障模型 ,对系统的状态和故障参数进行在线估计 ,然后将故障参数与修正的 Bayes分类算法 ( MB算法 )相结合 ,进行传感器故障在线检测、分离和估计。对连续搅拌釜式反应器( CSTR)的仿真结果表明 ,该集成故障诊断方法能够对多重传感器故障进行快速准确的分离和估计 。
An integrated fault detection and diagnosis approach to sensor faults based on radial basis function (RBF) neural networks is presented in this paper. An RBF neural network is used to estimate the state and fault parameters of the constructed model for sensor faults. The estimated fault parameters are processed by the improved Bayes algorithm to realize online sensor fault detection, isolation, and estimation. The simulation for continuous stirred tank reactor (CSTR) shows the presented approach can isolate and estimate the multiple sensor faults quickly and accurately and the integrated system has tolerant ability to sensor faults.
出处
《华东理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2002年第6期640-643,共4页
Journal of East China University of Science and Technology
关键词
故障诊断
RBF神经网络
状态估计
容错控制
fault detection and diagnosis
RBF neural network
state estimation
fault tolerant control
