笼型感应电机故障诊断技术综述

doi:10.11985/2023.01.001

摘要/Abstract

摘要：

作为整个电力系统最重要的环节之一，感应电机有着不可替代的作用。由于运行工况十分复杂，感应电机在使用过程中会出现各种类型的故障。为了保证电机正常运行，对电机早期故障进行诊断和监测显得尤为重要。考虑未来研究的需要，针对感应电机最常见的轴承故障、定子故障和转子故障三类故障，首先总结了每种故障常用诊断方法的优势及其局限性，其次基于最常用的振动信号、电机电流信号、声信号等特征分析技术，重点论述了当前电机故障特征提取的主流方法，对各常用故障诊断方法及其改进策略做了归纳和总结，对国内外研究现状进行了全面的论述和分析，总结了各种故障诊断方法的适用场景，并对该领域未来的研究方向进行了展望，旨在给未来的研究者提供依据和思路。

关键词: 感应电机, 轴承故障, 定子故障, 转子故障

Abstract:

As one of the most important links of the whole power system, induction motor plays an irreplaceable role. Because the operation condition is very complicated, there will be various types of failure in the use of induction motor. In order to ensure the normal operation of the motor, it is very important to diagnose and monitor the early failure of the motor. Considering the needs of future research, the most common bearing fault, stator fault and rotor fault of induction motor are studied. Firstly, the advantages and limitations of each fault diagnosis method are summarized. Secondly, based on the most commonly used vibration signal, motor current signal, acoustic signal and other feature analysis techniques, the current mainstream method of motor fault feature extraction is emphatically discussed. The common fault diagnosis methods and their improvement strategies are summarized, the research status at home and abroad is comprehensively discussed and analyzed, the application scenarios of various fault diagnosis methods are summarized, and the future research direction in this field is prospected, in order to provide the basis and ideas for future researchers.

Key words: Induction motor, bearing fault, stator fault, rotor fault

中图分类号:

TM343

石颉, 李莎莎, 申海锋. 笼型感应电机故障诊断技术综述[J]. 电气工程学报, 2023, 18(1): 1-13.

SHI Jie, LI Shasha, SHEN Haifeng. Review of Induction Motor Fault Diagnosis Technology[J]. Journal of Electrical Engineering, 2023, 18(1): 1-13.

图/表 4

表1

图1

表2

电机特征提取常用方法及其改进"

方法	原理	优势	文献	局限	改进
小波分解	通过对母小波进行缩放拉伸拟合原始波形	在时间上有很高的分辨能力	[16⇓-18]	小波基确定和分解级别的确定需要人为干预，参数选择不当会丢失有用信息；不能自适应调整波长	经验小波	自适应检测不同模式信号；
					离散小波	计算速度更快；
					连续小波	诊断精度更高；
					小波包分解	根据信号的特征频率选择合适的频带，能更好观察每个频带特征
经验模态分解	直接提取各种内在时间尺度相关能量，生成一组固有模态分量(IMF)	自适应处理技术，信号根据其固有特性进行分解，适用于复杂信号	[18⇓-20]	容易出现模态混叠问题	集合经验模态分解(EEMD)	添加有限幅值的白噪声，有助于频率尺度的自然分离，避免模态混叠问题
经验模态分解	直接提取各种内在时间尺度相关能量，生成一组固有模态分量(IMF)	自适应处理技术，信号根据其固有特性进行分解，适用于复杂信号	[18⇓-20]	容易出现模态混叠问题	互补集合经验模态分解(CEEMD)	筛选信息，丢弃不需要的信息成分，隔离相关信息
变分模态分解	基于维纳滤波和希尔伯特变换的自适应递归算法	适用于输入信号存在噪声问题，对噪声鲁棒性强	[21-22]	分解参数需要根据信号特征设置，需要大量经验	自适应变分模态分解	自适应获取最佳分解参数，可实现多故障分类和识别的准确性、稳定性
傅里叶变换	时域信号经过傅里叶变换到频谱图，通过检测频谱图判断是否发生故障	操作简单、计算时间短	[13, 23-24]	频率分辨率有限、频谱泄漏和低噪声容限，不适用于非平稳信号	短时傅里叶变换(STFT)	能在速度变化的情况下监测信号，克服傅里叶变换不适用于非平稳信号的缺点
多重信号分类	基于信号的谐波分解，在分解中可以识别噪声	对于短时信号具备高频率分辨能力	[24-25]	需要信号成分数量的先验知识，可能会产生杂散频率而导致错误解释	Gabor表示法	具有更高的噪声容限、更好的分辨率，不会产生杂散频率，信号频谱的质量更好
希尔伯特变换	在选择区域，分析该区域是否有故障相关的症状	避免加权窗口选择问题	[26-27]	适用于转子断条故障，对其他类型故障不是十分有效，不适用于非平稳信号	希尔伯特-黄变换(HHT)	能够有效分析非平稳信号

表2

图2

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