Title :
A New Filtering-Based Actuator Fault Diagnosis Approach Based on NN Models of PDFs
Author :
Zhang, Yu-Min ; Wu, Ling-Yao ; Guo, Lei ; Liu, Cheng-Liang
Author_Institution :
Res. Inst. of Autom., Southeast Univ., Nanjing
Abstract :
In many practical processes, the measured information is the stochastic distribution of the system output rather than its value. In this paper, following the new development for the fault diagnosis (FD) problem of stochastic processes (L. Guo and H. Wang, 2005), an improved FD method with Hinfin performance optimization is considered by using the output stochastic distributions. A multi-layer perceptron (MLP) neural network is adopted to approximate the probability density function (PDF) of the system outputs. The measure of estimation errors represented by the distances between two output PDFs, would be optimized to find the diagnosis filter gain. Simulation example is given for the weighting dynamics to demonstrate the effectiveness of the proposed method
Keywords :
Hinfin optimisation; fault diagnosis; filtering theory; linear matrix inequalities; multilayer perceptrons; stochastic processes; stochastic systems; Hinfin performance optimization; estimation errors; filtering-based actuator fault diagnosis; linear matrix inequalities; multi-layer perceptron neural network; probability density function; stochastic processes; Actuators; Estimation error; Fault diagnosis; Gain measurement; Multi-layer neural network; Multilayer perceptrons; Neural networks; Optimization; Probability density function; Stochastic processes;
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1614986
