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一种基于零序电流和改进对称点模式的感应电动机视觉故障检测方法

A Visual Fault Detection Method for Induction Motors Based on a Zero-Sequence Current and an Improved Symmetrized Dot Pattern.

作者信息

Huang Liangyuan, Wen Jihong, Yang Yi, Chen Ling, Shen Guoji

机构信息

College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.

Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology, Changsha 410073, China.

出版信息

Entropy (Basel). 2022 Apr 28;24(5):614. doi: 10.3390/e24050614.

DOI:10.3390/e24050614
PMID:35626499
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9141730/
Abstract

Motor faults, especially mechanical faults, reflect eminently faint characteristic amplitudes in the stator current. In order to solve the issue of the motor current lacking effective and direct signal representation, this paper introduces a visual fault detection method for an induction motor based on zero-sequence current and an improved symmetric dot matrix pattern. Empirical mode decomposition (EMD) is used to eliminate the power frequency in the zero-sequence current derived from the original signal. A local symmetrized dot pattern (LSDP) method is proposed to solve the adaptive problem of classical symmetric lattice patterns with outliers. The LSDP approach maps the zero-sequence current to the ultimate coordinate and obtains a more intuitive two-dimensional image representation than the time-frequency image. Kernel density estimation (KDE) is used to complete the information about the density distribution of the image further to enhance the visual difference between the normal and fault samples. This method mines fault features in the current signals, which avoids the need to deploy additional sensors to collect vibration signals. The test results show that the fault detection accuracy of the LSDP can reach 96.85%, indicating that two-dimensional image representation can be effectively applied to current-based motor fault detection.

摘要

电机故障，尤其是机械故障，在定子电流中表现出极为微弱的特征幅值。为了解决电机电流缺乏有效直接信号表征的问题，本文介绍了一种基于零序电流和改进对称点阵模式的感应电机视觉故障检测方法。采用经验模态分解（EMD）消除从原始信号导出的零序电流中的工频分量。提出了一种局部对称点阵模式（LSDP）方法来解决经典对称点阵模式对异常值的自适应问题。LSDP方法将零序电流映射到最终坐标，得到比时频图像更直观的二维图像表征。使用核密度估计（KDE）进一步完善图像密度分布信息，以增强正常样本与故障样本之间的视觉差异。该方法从电流信号中挖掘故障特征，避免了部署额外传感器来采集振动信号的需求。测试结果表明，LSDP的故障检测准确率可达96.85%，表明二维图像表征可有效应用于基于电流的电机故障检测。

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