基于特征融合的提升机逆变器故障诊断

Feature fusion based fault diagnosis of hoist inverter

  • 摘要: 矿井提升机逆变器故障诊断的难点在于提取表征故障的特征,目前主要利用信号处理方法得到故障统计特征,或通过神经网络提取故障深度特征。提升机逆变器在实际工作环境中,受背景噪声和负载变化等因素影响,运用单一的特征提取方法难以获得能有效表征故障的特征,导致提升机逆变器故障诊断准确率低。针对上述问题,提出了一种基于统计特征与深度特征融合的提升机逆变器故障诊断方法。首先,利用希尔伯特-黄变换(HHT)对逆变器输出电流信号进行优化集合经验模态分解(MEEMD),提取故障统计特征,同时利用压缩激励密集连接卷积网络(SE-DenseNet)提取输出电流信号的深度特征;然后,利用局部线性判别分析(LFDA)对2种特征的组合进行融合降维处理,得到统计特征和深度特征的低维融合特征;最后,将低维融合特征输入极限学习机,实现逆变器故障分类。针对提升机逆变器中单个IGBT开路故障进行实验,结果表明,该方法得到的低维融合特征比单一特征的故障表征能力更强,有效提高了故障识别准确率。

     

    Abstract: The difficulty in fault diagnosis of mine hoist inverters lies in extracting the features that characterize faults. At present, signal processing methods are mainly used to obtain fault statistical features, or the fault depth features are extracted by neural networks. In the actual working environment, the hoist inverter is affected by factors such as background noise and load changes. Therefore, it is difficult to obtain features that can characterize the faults effectively by using a single feature extraction method, resulting in low fault diagnosis accuracy of the hoist inverter. In order to solve the above problems, a fault diagnosis method of hoist inverter based on the fusion of statistical features and depth features is proposed. Firstly, the Hilbert-Huang transform(HHT) is used to conduct modified ensemble empirical mode decomposition(MEEMD) of the inverter output current signal so as to obtain the fault statistical features. At the same time, the squeeze and excitation with densely connected convolutional network(SE-DenseNet) is used to extract the depth features of the output current signal. Secondly, the local fisher discriminant analysis(LFDA) is used to perform fusion and dimensionality reduction processing on the combination of the two features to obtain low-dimensional fusion features of statistical features and depth features. Finally, the low-dimensional fusion features are input to the extreme learning machine to obtain inverter fault classification. Experiments are conducted for a single IGBT open-circuit fault in the hoist inverter. The results show that the low-dimensional fusion features obtained by this method are more capable of fault characterization than single features, which improves the fault recognition accuracy effectively.

     

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