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提出了一种基于小波和人工神经网络的故障检测与诊断的方法 ,该方法利用小波包分解的精确细分的特点 ,分别对正常系统和故障系统的采样信号进行精确特征提取 ,并构造一系列基于信号能量且具有表征系统状态能力的特征向量 ,然后利用人工神经网络分类器对系统在各种状态下的特征向量进行分类决策 ,从而实现对系统的故障检测与诊断
A method of fault detection and diagnosis based on wavelet and artificial neural network is proposed. By using the characteristics of precise subdivision of wavelet packet decomposition, this method extracts the accurate features of the sampled signals of normal system and fault system, and constructs a series of Based on the signal energy and the eigenvector which has the ability of characterizing the state of the system, the artificial neural network classifier is used to classify and decide the eigenvectors of the system in various states so as to realize the system fault detection and diagnosis