基于小波神经网络的组合故障模式识别

PATTERN RECOGNITION FOR COMPOSITE FAULT BASED ON WAVELET NEURAL NETW0RKS

提出了一种基于小波神经网络的组合故障模式识别方法 .针对以歼击机为代表的非线性系统中存在的多重并发故障 ,构造了一个多层的小波神经网络 ,在输入层对残差信号进行二进离散小波变换 ,提取其在多尺度下的细节系数作为故障特征向量 ,并将其输入到神经网络分类器进行相应的模式分类 .仿真结果表明 ,本文方法为多重并发故障的诊断提供了有效的方法和途径 .

A pattern recognition method for composite fault diagnosis based on wavelet neural networks is presented. Considering the multiple faults in complex nonlinear system such as a fighter, a multiple-layer wavelet neural network is constructed. Residual signal is processed by discrete binary wavelet transformation at the input layer, and the detail coefficients are obtained under multi-resolution as fault character vectors, finally these character vectors are sent to the neural network classifier to complete fault pattern recognition. Simulation results reveal that the presented method is effective for composite fault diagnosis.