基于参数优化变分模态分解的交流变频电机转子断条故障识别方法

doi:10.19595/j.cnki.1000-6753.tces.200572

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摘要该文提出一种基于变分模态分解（VMD）和Park变换的交流变频电机早期转子断条故障识别方法。重点对低速状态下电机早期断条故障特征频率识别展开研究,基于电流信号特点结合萤火虫优化算法（FA）设定变分模态分解参数,进一步强化其自适应分解能力,并在此基础上获取三相电流的基频分量信息,通过Park变换检测早期故障特征。仿真与实验结果表明,该方法检测电机早期转子断条故障特征优于经验模态分解（EMD）方法,并可实现故障特征频率的早期定位,这对电机的故障辨识和预警具有重要支撑意义。

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	李睿彧
	刘飞
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	徐光华

关键词 ：交流变频电机, 转子断条故障, 变分模态分解, 萤火虫优化算法, Park变换

Abstract：This paper presents a novel detection method for broken rotor bar fault in variable frequency AC motor based on variational mode decomposition (VMD) and Park transformation. The identification method of the fault characteristic frequency under the low-speed state was further studied. Combined with the characteristics of current signals and the firefly algorithm (FA), the adaptive decomposition ability of VMD was strengthened. On this basis, the supply frequency components of the three-phase current were extracted and the fault features were detected through Park transformation. Finally, simulation and experimental results show that the proposed method is superior to the empirical mode decomposition (EMD) for detecting early fault features and can realize the identification of the early fault frequencies, which is a great significance for the early fault detection and forecast of the variable frequency AC motor.

Key words： Variable frequency AC motor broken rotor bars variational mode decomposition (VMD) firefly algorithm (FA) Park transformation

收稿日期: 2020-05-29

PACS:

TM343

基金资助:国家重点研发计划项目（2018YFB2000202）、国家自然科学基金项目（51775423）、中国博士后科学基金特别项目（2018T111046）、中国博士后科学基金项目（2017M623159）、陕西省博士后科学基金项目（2017BSHEDZZ68）和西安交通大学基本科研业务费（XJJ2018047）资助

通讯作者: 刘飞男,1979年生,博士,助理研究员,研究方向为机械系统动态测试及状态评估技术。E-mail: fayelau@xjtu.edu.cn

作者简介: 李睿彧男,1995年生,硕士研究生,研究方向为机械设备故障诊断技术。E-mail: lry475517@stu.xjtu.edu.cn

引用本文:

李睿彧, 刘飞, 梁霖, 罗爱玲, 徐光华. 基于参数优化变分模态分解的交流变频电机转子断条故障识别方法[J]. 电工技术学报, 2021, 36(18): 3922-3933. Li Ruiyu, Liu Fei, Liang Lin, Luo Ailing, Xu Guanghua. Fault Identification of Broken Rotor Bars for the Variable Frequency AC Motor Based on Parameter Optimized Variational Mode Decomposition. Transactions of China Electrotechnical Society, 2021, 36(18): 3922-3933.

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