基于Choquet模糊积分的水电机组振动故障诊断

为提高水电机组故障诊断的准确性,提出了基于Choquet模糊积分融合的多分类器组合故障诊断方法,对朴素贝叶斯分类器进行了基于属性相似度的加权改进,得到基于属性相似度的加权朴素贝叶斯分类器(Attribute Similarity Weighted Naive Bayes Classifier,简称SWNBC),并应用基于Mahalanobis距离的分类器(Mahalanobis Distance Classifier,简称MDC)与BP神经网络(BP neural network)组合成为SWNBC+MDC+BP的多分类器组合模型,以小波包提取的相关频带能量作为输入特征向量,应用组合模型对水电机组故障进行诊断,采用模糊积分法来决定最终的故障类型。实验结果表明该模型相对于单一的分类器,能有效提高识别故障的精度。

Vibration Fault Diagnosis of Hydro- generating Unit Base on Choquet Integrate

A new method of Vibration hydro-generating Unit fault diagnosis was proposed base on Choquet Integrate combined classifier for improve fault diagnosis accuracy, improve Naive Bayes Classifier base on Attribute Similarity Weighted Naive Bayes Classifier(SWNBC),use Mahalanobis Distance Classifier(MDC) and BP neural network (BP) composed combined classifier SWNBC+MDC+BP, wavelet packet extraction of related band energy as input characteristic vector.Applicate combined classifier for fault diagnosis, fuzzy integral to make the final fault type. The simulation experiment shows that the model is better than the single classifier, can effectively identify fault.