Hilbert解调制方法诊断异步电机轴承故障
Diagnosis of Bearing Fault in Induction Motors Using Hilbert Demodulation Approach
查看参考文献20篇
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文摘
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针对电机电流信号特征分析(MCSA)方法诊断轴承外圈故障时易受到基频频谱泄露和偏心谐波以及供电系统噪声的影响,使得MCSA方法无法诊断低负载运行条件下的轴承外圈故障问题,提出一种基于Hilbert解调方法提取定子电流平方包络线的诊断方法。首先使用Hilbert变换构造定子电流解析信号,并提取定子电流解析信号的平方包络线;然后对提取的平方包络线做快速傅里叶变换(FFT)分析;最后根据FFT频谱中是否存在故障特征频率fof判断轴承是否发生故障。该方法能够将故障特征频率的检测从传统的边频带成分|f1±fof|转换为对轴承外圈故障特征频率fof的直接检测,能够有效消除基频频谱泄露和供电系统强噪声的干扰。电机在不同负载运行状态下的实验结果均验证了所提方法的有效性和稳定性。 |
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其他语种文摘
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Because of the influence of the spectral leakage of the main frequency component, eccentricity harmonics and power supply noise, motor current signature analysis (MCSA) is unable to detect the bearing outer raceway fault when an induction motor (IM) operates under the light load conditions. In this paper, a bearing fault diagnosis method based on the squared envelope approach of the motor stator current using Hilbert demodulation technique was proposed. An analytical signal corresponding to the stator current signal was constructed first using Hilbert transform and then a squared envelope was obtained from the analytical signal. Second, the Fast Fourier Transform (FFT) of the squared envelope was investigated. Finally, the motor bearing failure was determined based on whether the characteristic fault frequency fof could be found in the squared envelope spectrum or not. The proposed method can be used to detect the characteristic fault frequency fof directly instead of detecting the sideband components around the supply frequency |f1±fof| as used in traditional MCSA. Thus, the proposed approach can significantly reduce the negative influence of the spectral leakage of the main frequency component spectral and the power supply noise. The experimental results under different load conditions clearly prove the effectiveness and stability of the proposed method. |
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来源
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电工技术学报
,2018,33(21):4941-4948 【核心库】
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DOI
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10.19595/j.cnki.1000-6753.tces.171571
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关键词
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异步电机
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轴承故障
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Hilbert变换
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定子电流
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地址
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1.
中国科学院沈阳自动化研究所, 沈阳, 110016
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中国科学院大学, 北京, 100049
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辽宁科技大学电子与信息工程学院, 鞍山, 114051
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1000-6753 |
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学科
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电工技术 |
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基金
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国家重点研发计划项目
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中国科学院重点部署项目
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文献收藏号
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CSCD:6359912
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参考文献 共
20
共1页
|
|
1.
Leite V C M N. Detection of localized bearing faults in induction machines by spectral kurtosis and envelope analysis of stator current.
IEEE Transactions on Industrial Electronics,2015,62(3):1855-1865
|
被引
7
次
|
|
|
|
|
2.
曹朋朋. 异步电机基于MRAC的转子时间常数在线辨识算法的统一描述.
电工技术学报,2017,32(19):62-70
|
被引
4
次
|
|
|
|
|
3.
刘璐. 考虑磁饱和的感应电机MTPA转矩控制.
电工技术学报,2017,32(23):42-50
|
被引
6
次
|
|
|
|
|
4.
Li Dezhi. A spectrum synch technique for induction motor health condition monitoring.
IEEE Transactions on Energy Conversion,2015,30(4):1348-1355
|
被引
3
次
|
|
|
|
|
5.
Maruthi G S. Application of MEMS Accelerometer for detection and diagnosis of multiple faults in the roller element bearings of three phase induction motor.
IEEE Sensors Journal,2016,16(1):145-152
|
被引
1
次
|
|
|
|
|
6.
He Wangpeng. Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform.
Mechanical Systems and Signal Processing,2015,54/55:457-480
|
被引
11
次
|
|
|
|
|
7.
潘作为. 基于复数小波多尺度包络分析的风机滚动轴承故障特征提取.
中国电机工程学报,2015,35(16):4147-4152
|
被引
7
次
|
|
|
|
|
8.
杨明. 基于电机驱动系统的齿轮故障诊断方法综述.
电工技术学报,2016,31(4):58-63
|
被引
11
次
|
|
|
|
|
9.
Imaouchen Y. Bearing fault detection using motor current signal analysis based on wavelet packet decomposition and Hilbert envelope.
AVE2014-4th Colloque Analyse Vibratoire Experimentale/Experimental Vibration Analysis,2014:1-5
|
被引
2
次
|
|
|
|
|
10.
Amirat Y. Hilbert transform-based bearing failure detection in DFIGBased wind turbines.
International Review of Electrical Engineering,2011,6(3):1249-1256
|
被引
1
次
|
|
|
|
|
11.
Zarei J. An advanced Park's vectors approach for bearing fault detection.
Tribology International,2009,42(2):213-219
|
被引
4
次
|
|
|
|
|
12.
薛征宇. 基于Park矢量的电动机轴承故障检测方法.
轴承,2012(4):50-55
|
被引
1
次
|
|
|
|
|
13.
Salem S B. Support vector machine based decision for mechanical fault condition monitoring in induction motor using an advanced Hilbert-Park transform.
ISA Transactions,2012,51(5):566-572
|
被引
6
次
|
|
|
|
|
14.
杨江天. 基于定子电流小波包分析的牵引电机轴承故障诊断.
铁道学报,2013(2):32-36
|
被引
11
次
|
|
|
|
|
15.
Li B. Neural-networkbased motor rolling bearing fault diagnosis.
IEEE Transactions on Industrial Electronics,2000,47(5):1060-1069
|
被引
24
次
|
|
|
|
|
16.
Schiltz R L. Forcing frequency identification of rolling element bearings.
Sound and Vibration,1990,24(5):16-19
|
被引
1
次
|
|
|
|
|
17.
Schoen R R. Motor bearing damage detection using stator current monitoring.
IEEE Transactions on Industry Applications,1995,31(6):1274-1279
|
被引
16
次
|
|
|
|
|
18.
车香芝. 基于齿谐波检测的无速度传感器感应电机的转速估计.
2008中国仪器仪表与测控技术报告大会,2008:414-417
|
被引
1
次
|
|
|
|
|
19.
喻敏. 同步挤压小波变换在电力系统低频振荡模态参数提取中的应用.
电工技术学报,2017,32(6):14-20
|
被引
14
次
|
|
|
|
|
20.
朱永利. 基于改进变分模态分解和Hilbert变换的变压器局部放电信号特征提取及分类.
电工技术学报,2017,32(9):221-235
|
被引
40
次
|
|
|
|
|
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