6kV高压电动机故障智能诊断方法

Intelligent Diagnosis Method for Fault of 6 kV High-Voltage Motor

针对6 kV高压电动机故障智能诊断方法存在错诊率较高和诊断响应时间较长的问题,提出6k V高压电动机故障智能诊断方法。利用无线传感器采集电动机运行数据,利用降噪自编码网络模型对数据去噪处理,利用小波包分析技术获取数据小波包能量熵信息,提取数据特征,根据计算故障特征频率,诊断电动机故障类型,以完成6 kV高压电动机故障智能诊断。实验证明,该设计方法错诊率在1%以内,能在1s内完成故障诊断,具有良好的应用前景。

Aiming at the problems of high misdiagnosis rate and long diagnostic response time in the intelligent diagnosis method for 6 kV high-voltage motor faults,a 6 kV high-voltage motor fault intelligent diagnosis method is proposed.Using wireless sensors to collect motor operation data,using the noise reduction self coding network model to denoise the data,using wavelet packet analysis technology to obtain wavelet packet energy entropy information,extracting data features,and diagnosing motor fault types based on calculated fault feature frequencies,in order to achieve intelligent diagnosis of 6 kV high-voltage motor faults.Experimental results have shown that the misdiagnosis rate of this design method is within 1%,and it can complete fault diagnosis within 1 s,which has good application prospects.