基于变模态分解的异步电机转子断条故障诊断

doi:10.19595/j.cnki.1000-6753.tces.220124

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摘要

电机稳态运行时的故障特征频率与基频接近,难以实现转子断条时的故障电流分量的有效提取。对此,该文根据起动过程中转子断条故障特征信号频率易与基频区分的特点,采用变模态分解（VMD）方法对起动过程中的定子电流进行分析并对故障进行诊断。首先,基于平均瞬时频率对VMD的模态个数进行优化,准确分解出能量集中的断条故障特征信号。在此基础上,利用维格纳准概率分布高时频分辨率的特点绘制断条故障特征信号的时频分布图,引入大津算法对图片进行抗噪处理,突出故障特征部分。然后,以故障特征信号能量值作为故障量化因子,以不同故障状态下多组实验数据的均值和标准差为依据设置阈值,实现系统自动故障预警的目的。最后,在一台5.5 kW异步电机上进行了实验,结果表明,所提诊断方法不仅能够实现包括不完全断条在内的故障诊断,还能够实现对断条故障严重程度的判断。

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	夏志凌
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关键词 ：异步电机, 转子断条, 故障诊断, 变模态分析（VMD）, 本征模态函数（IMF）, 大津算法（OTSU）

Abstract：

When the induction motor is operated under the steady-state operating condition with a light load, the fault characteristic frequency of the broken bar fault is close to the fundamental frequency, making it difficult to extract the fault current component from the stator current effectively. Moreover, the fault characteristic signal energy shares a fairly minor fraction of the total stator current energy in the early stages of incomplete broken bars, which increases the difficulty of fault current component extraction. In order to improve the diagnosis effect of the broken bar fault at the early stage when the motor is running at light load, according to the characteristics of distinguishing the characteristic signal of the broken bar fault from the fundamental signal during the starting period, this paper proposes the variational mode decomposition (VMD) method to process the stator start-up current, and the broken bar fault diagnosis is carried out by utilised the energy value of the fault characteristic current obtained by decomposition.
The proposed broken bar fault diagnosis scheme is mainly divided into three stages: stator current pre-processing, fault characteristic current extraction, fault characteristic current energy time-frequency distribution calculation and fault severity quantification. In the first stage, the Clarke transformation is applied to obtain the current space vector, and the amplitude variation of the current space vector during the starting process is utilised to adaptively extract the stator current for analysis, and the influence of high frequency interference on low frequency signal is removed by an anti-aliasing filter. In the second stage, a VMD algorithm based on the optimisation of the mean instantaneous frequency is proposed to extract the left frequency component of the fault characteristic current. In order to optimise the extraction effect, it is necessary to determine the appropriate parameters for the penalty factor and the number of modes in the VMD algorithm. The penalty factor should take into account the narrow-band nature of the fundamental signal and the broad-band nature of the fault characteristic current, and have low sensitivity to the change of the energy value of the fault characteristic current to accommodate to the diagnosis under different fault degree. Concerning the number of modes, in this paper we propose to determine the optimal number of modes to ensure the energy concentration of the fault characteristic current by exploiting the variation law of the centre frequency and the mean instantaneous frequency of the sub signals obtained from the VMD decomposition. In the third stage, Wigner-Ville Distribution (WVD) is utilised to extract the energy time-frequency distribution of the fault characteristic current, and Otsu's method (OTSU) is applied to perform anti-noise processing on the time-frequency distribution to highlight the fault characteristic information. In order to further improve the disturbance rejection performance of the diagnosis scheme, we take the energy value of the characteristic curve of the right part of the left frequency component as the fault quantization factor, and set the threshold based on the mean and standard deviation of multiple groups of experimental data under different fault states, so as to realise the purpose of automatic fault warning.
In this paper, the experimental verification is carried out on a 5.5 kW induction motor. The results show the proposed fault diagnosis scheme performs well in terms of interference rejection and is able to extract the fault characteristic current component in the condition of incomplete broken bars fault at high noise level. Moreover, the energy value of the right part of the fault characteristic signal has a strong positive correlation with the fault severity, which can perform the rotor broken bar fault detection well and can realise the discrimination of different rotor broken bar fault severity including incomplete broken bars.

Key words： Induction motor broken bars fault diagnosis variable mode decomposition (VMD) intrinsic mode function (IMF) Otsu's method (OTSU)

收稿日期: 2022-01-24

PACS:

TM307

基金资助:

浙能集团科技资助项目（208020210582）

通讯作者: 史婷娜女,1969年生,博士,教授,研究方向为电机系统及其控制。E-mail: tnshi@zju.edu.cn

作者简介: 夏志凌男,1980年生,高级工程师,研究方向为异步电机故障诊断。E-mail: xiazhiling@163.com

引用本文:

夏志凌, 胡凯波, 刘心悦, 李彬华, 史婷娜. 基于变模态分解的异步电机转子断条故障诊断[J]. 电工技术学报, 2023, 38(8): 2048-2059. Xia Zhiling, Hu Kaibo, Liu Xinyue, Li Binhua, Shi Tingna. Fault Diagnosis of Rotor Broken Bar in Induction Motor Based on Variable Mode Decomposition. Transactions of China Electrotechnical Society, 2023, 38(8): 2048-2059.

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