基于小波包的开关电流电路故障诊断

为提高开关电流电路故障诊断的精度,提出了一种基于小波包优选和优化BP神经网路的开关电流电路特征抽取与识别方法.首先对开关电流电路原始响应信号进行多层次的小波包分解,接着计算N层分解后的归一化能量值,以特征偏离度作为评价选择最优小波包基,构建最优故障特征向量,最后将提取的最优故障特征通过遗传算法优化的BP神经网络进行分类.该方法以实例电路进行验证,结果表明所有的软故障均得到了有效的分类,说明了该方法在开关电流电路故障诊断中的优越性. 

Fault detection in switched current circuits based on preferred wavelet packet

In order to improve the accuracy of switched current circuit fault diagnosis, a feature extraction and recognition method of switched current circuit based on wavelet packet optimization and optimization of BP neural network was proposed. Firstly, the wavelet packet decomposition of the original response signal of the switched current circuit was carried out. Then, the normalized energy value after the decomposition of the N layer was calculated, and the optimal wavelet packet basis was selected by using the characteristic deviation as the evaluation. Finally, the optimal fault feature vector was constructed. The extracted optimal fault characteristics were classified by BP neural network optimized by genetic algorithm. The results of this method were verified by the example circuit. The results show that all the soft faults are effectively classified, and the superiority of the method in the fault diagnosis of the switched current circuit is illustrated.