基于阵列漏磁信号分析的无刷直流电机高阻接触故障诊断研究

Fault diagnosis of high resistance connection in brushless DC motor based on analysis of array leakage flux signals

无刷直流电机具有效率高、能量密度高、噪音低等优点进而被广泛应用于汽车、工业自动化、航空航天等领域。高阻接触(high resistance connection,HRC)故障是电机的典型故障之一,该故障严重时会导致急剧温升乃至火灾,因而无刷直流电机的HRC故障研究具有重要的意义。通常采用电流电压信号分析的方法诊断HRC故障,但现有的方法仍存在局限与不足。针对此问题,设计了一种新的结合阵列漏磁信号分析和机器学习的方法实现无刷直流电机HRC故障的定位和定量分析。首先通过安装在电机外壳的霍尔传感器阵列采集多通道漏磁信号,利用神经网络分析漏磁信号的时域特征实现电机HRC故障检测和定位。在确定故障相之后,利用另一个神经网络模型分析漏磁信号频域特征实现HRC故障的定量分析。实验结果表明,提出的方法检测和定位故障的精度为98.75%,故障定量分析的平均均方根误差为0.018Ω。该方法具有非侵入式测量、易于实现、效率高等优点,对提升无刷直流电机HRC故障检测精度和效率具有促进作用。

Brushless DC motors(BLDCMs)have been intensively used in automobiles,industry automations,and aeronautics and astronautics due to their advantages including high efficiency,high power density,and low noise.High resistance connection(HRC)fault is one of the typical motor faults.A severe HRC fault will cause serious temperature rise and even fire,and hence diagnosis of HRC fault in BLDCM is of significant.Generally,the HRC faults are detected by analyzing the motor current and voltage signals.However,there still deficiencies in the existed methods.This study designs a new method that combines the analysis of the array leakage flux signals and machine learning technology to realize location and quantitative analysis of HRC fault in BLDCM.First,multi-channels of flux signals captured by a Hall sensor array that installed on the motor shell are sampled.A neural network model based on the time-domain features is designed to detect and localize the HRC faults.Subsequently,another neural network model based on the frequency-domain features is used to quantitatively analyze the HRC fault degree.The experimental results indicated that the accuracy of fault detection and localization is 98.75% and the averaged root mean square error of quantitative analysis is 0.018Ω.The proposed method is noninvasive,easy to implement with high efficiency,hence,it will improve the accuracy and efficiency of HRC fault detection in BLDCM.