电工技术学报  2016, Vol. 31 Issue (19): 132-140    DOI:
电机与电器 |
基于电机驱动系统的齿轮故障诊断方法   对比研究
杨明,柴娜,李广,李雨琪,徐殿国
哈尔滨工业大学电气工程系 哈尔滨 150001
A Comparative Study of Gear Fault Diagnosis Methods Based on the Motor Drive System
Yang Ming, Chai Na, Li Guang, Li Yuqi, Xu Dianguo
Department of Electrical Engineering Harbin Institute of Technology Harbin 150001 China
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摘要 电机的电磁转矩、定子电流等信号可以反映负载转矩的变化,故而在采用电机驱动的齿轮传动系统中,可直接利用电机本体作为传感器来实现齿轮故障的无损诊断。建立了电机、齿轮一体化机电系统模型,对电磁转矩分析法(ETSA)和电机电流特征分析法(MCSA)的故障诊断原理进行了分析与仿真验证,发现电磁转矩信号不受电流基波的影响,更能直观地体现出故障信息;且故障特征在频域下比时域下更为明显。实验平台综合对比了不同转速和负载转矩下两种方法的诊断效果。结果表明两种方法均受转速和负载转矩影响较大,低速重载有利于故障诊断的进行;但ETSA比MCSA适用的转速范围更广。
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杨明
柴娜
李广
李雨琪
徐殿国
关键词 电磁转矩分析法电机电流特征分析法齿轮故障诊断    
Abstract:In the gearbox-based electromechanical system,some electrical signals,such as the electromagnetic torque and the motor current,can track the pulsations of the load torque,which contributes to the fact that the motor can be considered as a non-destructive sensor to diagnose gear faults.Firstly,this paper establishes an integrated electromechanical model of the motors and gear drive system.Then,the theories of the electromagnetic torque signature analysis(ETSA) and the motor current signature analysis(MCSA) for fault diagnosis are analyzed and verified through simulation.It can be seen that the electromagnetic torque,which is free of the main frequency,can reflect the failure information more intuitively.Also,the fault features in frequency domain are more obvious than those in time domain.Finally,the performances of these two methods are compared at different speed and load torque conditions by experiment.The results show that both methods are influenced by speed and load torque.It would be better to perform fault diagnosis under low speed and heavy load.However,ETSA is effective in a wider range than MCSA.
Key wordsElectromagnetic torque signature analysis(ETSA)    motor current signature analysis(MCSA)    gear    fault diagnosis   
收稿日期: 2015-05-30      出版日期: 2016-11-02
PACS: TM315  
基金资助:国家科技重大专项资助项目(2012ZX04001051)。
作者简介: 杨 明 男,1978年生,博士,教授,博士生导师,研究方向为电力电子技术及交流伺服系统与智能控制。E-mail:yangming@hit.edu.cn(通信作者)柴 娜 女,1993年生,博士研究生,研究方向为伺服电机在齿轮故障诊断中的应用。E-mail:chaina_hit@163.com
引用本文:   
杨明,柴娜,李广,李雨琪,徐殿国. 基于电机驱动系统的齿轮故障诊断方法   对比研究[J]. 电工技术学报, 2016, 31(19): 132-140. Yang Ming, Chai Na, Li Guang, Li Yuqi, Xu Dianguo. A Comparative Study of Gear Fault Diagnosis Methods Based on the Motor Drive System. Transactions of China Electrotechnical Society, 2016, 31(19): 132-140.
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