摘要
提出了一种基于多维特征和多分类器的水电机组故障诊断方法。通过提取水电机组不同状态下振动信号的时域特征、频域特征和集合经验模态分解-样本熵,构建多维特征,实现特征信息的多维互补,并利用遗传算法对构建的多维特征进行降维处理。以此多维特征作为分类器的输入,分别通过支持向量机、反向传播神经网络和朴素贝叶斯分类器进行故障诊断,将三种分类器的初步诊断结果进行融合得到最终诊断结论,从而提高水电机组故障诊断的准确率。为验证该方法的有效性,将转子不平衡、转子不对中、转子碰磨等故障在转子试验台上进行模拟,并用上述方法进行诊断,结果表明,较单维特征和单分类器,多维特征输入和多分类器融合的故障诊断准确率更高。
A fault diagnosis method of hydroelectric generating sets based on multi-dimensional features and multiple classifiers is developed.Multi-dimensional features are constructed by extracting time domain characteristics,frequency domain characteristics,and sample entropy of ensemble empirical mode decomposition from the vibration signals of the generating units in different working conditions,and reduced by the genetic algorithm,so that this new method can achieve multidimensional information complementarity in the vibration features.With the multi-dimensional features as classifier inputs,faults are diagnosed using the support vector machine classifier,back propagation neural network classifier,and naive Bayes classifier.The preliminary diagnosis results of the three classifiers are fused to draw the final diagnosis conclusion,thus improving the accuracy of fault diagnosis of the generating sets.To verify the method,rotor unbalance,rotor misalignment and rotor rubbing are simulated experimentally on a rotor test bench,and the method is used to diagnose these faults.The results show that the diagnosis accuracy of multi-dimensional features and multiple classifiers is much higher than that of the single dimension feature and single classifier.
作者
程晓宜
陈启卷
王卫玉
郑阳
郭定宇
娄强
CHENG Xiaoyi;CHEN Qijuan;WANG Weiyu;ZHENG Yang;GUO Dingyu;LOU Qiang(Key Laboratory of Transients in Hydraulic Machinery,Ministry of Education,Wuhan University,Wuhan 430072)
出处
《水力发电学报》
EI
CSCD
北大核心
2019年第4期179-186,共8页
Journal of Hydroelectric Engineering
基金
国家自然科学基金(51379160)
关键词
多维特征
多分类器
样本熵
水电机组
故障诊断
multi-dimensional features
multiple classifiers
sample entropy
hydroelectric generating set
fault diagnosis
