基于支持向量机与粗糙集的隔爆电动机故障诊断

Fault diagnosis of explosion proof motor based on SVM and RS

  • 摘要: 针对煤矿井下隔爆电动机故障数据获取难且故障数据杂乱、非线性等问题,提出了一种基于支持向量机与粗糙集的隔爆电动机故障诊断方法。该方法采用小波包对隔爆电动机定子瞬时功率进行频谱分析,并提取故障特征量;利用粗糙集的约简特性消除故障特征量冗余数据,将约简后的故障特征量作为支持向量机的输入样本,实现隔爆电动机转子故障诊断和分类。仿真结果表明,该方法故障诊断结果准确率达到92.857 1%。

     

    Abstract: In view of problems that fault data acquisition of explosion proof motor in underground coal mine was difficult and fault data was clutter and nonlinear, a fault diagnosis method of explosion proof motor based on SVM and RS was proposed. Wavelet packet is used to analyze instantaneous power of stator of explosion proof motor, and extract fault feature. Redundant data of fault feature is eliminated using reduction feature of rough set, and the reduced feature is used as input sample of support vector machine, to realize rotor fault diagnosis and classification of explosion proof motor. The simulation results show that fault diagnosis accuracy of the proposed method reaches 92.857 1%.

     

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