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基于多尺度引导滤波和决策融合的电力设备热故障诊断方法研究

梁剑 黄志鸿 张可人

梁剑, 黄志鸿, 张可人. 基于多尺度引导滤波和决策融合的电力设备热故障诊断方法研究[J]. 红外技术, 2022, 44(12): 1344-1350.
引用本文: 梁剑, 黄志鸿, 张可人. 基于多尺度引导滤波和决策融合的电力设备热故障诊断方法研究[J]. 红外技术, 2022, 44(12): 1344-1350.
LIANG Jian, HUANG Zhihong, ZHANG Keren. Multi-scale Guided Filter and Decision Fusion for Thermal Fault Diagnosis of Power Equipment[J]. Infrared Technology , 2022, 44(12): 1344-1350.
Citation: LIANG Jian, HUANG Zhihong, ZHANG Keren. Multi-scale Guided Filter and Decision Fusion for Thermal Fault Diagnosis of Power Equipment[J]. Infrared Technology , 2022, 44(12): 1344-1350.

基于多尺度引导滤波和决策融合的电力设备热故障诊断方法研究

基金项目: 

国网湖南省电力有限公司科技项目 5216A522000U

详细信息
    作者简介:

    梁剑(1972-),男,湖南衡阳人,硕士,高级工程师,主要研究方向为电力人工智能,电力设备带电检测。E-mail: 952897509@qq.com

    通讯作者:

    黄志鸿(1993-),男,湖南长沙人,博士,高级工程师,主要研究方向为电力设备故障智能诊断、红外图像处理。E-mail: zhihong_huang111@163.com

  • 中图分类号: TP751.1

Multi-scale Guided Filter and Decision Fusion for Thermal Fault Diagnosis of Power Equipment

  • 摘要: 本文提出一种基于多尺度引导滤波和决策融合(multi-scale guided filter and decision fusion, MGDF)的电力设备热故障诊断方法,联合多尺度引导滤波和决策融合技术,充分挖掘红外图像的空间结构信息和温度信息。该方法有3个主要步骤。首先,基于热故障区域与环境背景在红外图像上的温度差异特性,逐像素计算热故障区域与环境背景的马氏距离,获取初始的热故障诊断结果。然后,采用不同参数设置的引导滤波器对初始诊断结果进行滤波处理,并将生成的若干引导滤波特征图堆叠在一起。不同参数下的滤波特征图包含着互补的空间结构信息。最后,为充分挖掘不同尺度特征图的空间结构信息和温度差异信息,利用主成分分析法对引导滤波特征图进行决策融合,提升热故障的诊断精度,生成最终的热故障诊断结果图。实验测试结果表明,本文方法在热故障诊断精度上有明显优势,满足电力设备红外巡检的应用需求。
  • 图  1  所提出的MGDF方法流程

    Figure  1.  The flowchart of the proposed MGDF method

    图  2  初始发热故障诊断结果

    Figure  2.  Initial thermal fault diagnosis result

    图  3  两个滤波参数εr的影响:(a) 初始热故障诊断结果;(b)-(e)不同参数下的滤波特征;和(f)最终热故障诊断结果

    Figure  3.  Influence of the two parameters, r and ε to the performance of the gulied filter: (a) Initial fault diagnosis result; (b) - (e) Filterd feature maps with different parameter settings, and (f) Finial fault diagnosis result

    图  4  不同方法在第一幅测试图的诊断结果

    Figure  4.  Different diagnosis results on the first test image

    图  5  不同方法在第二幅测试图的诊断结果

    Figure  5.  Different diagnosis results on the second test image

    图  6  不同方法在第三幅测试图的诊断结果

    Figure  6.  Different diagnosis results on the third test image

    表  1  不同诊断方法的AUC指标

    Table  1.   AUC values of different diagnosis methods

    Test
    images
    RX LDP LRR MGDF
    1 0.9707 0.8312 0.8974 0.9852
    2 0.9901 0. 9132 0. 9326 0.9993
    3 0.9893 0. 9253 0. 9486 0.9989
    下载: 导出CSV

    表  2  不同诊断方法的运行时间

    Table  2.   Running time of different diagnosis methods

    Test images RX LDP LRR MGDF
    1 0.59 0.75 0.47 2.31
    2 0.53 0.61 0.34 2.27
    3 0.84 0.98 0.56 2.15
    下载: 导出CSV

    表  3  单一尺度滤波参数的AUC指标

    Table  3.   Diagnosis methods with various parameters

    Test images GF1 GF2 GF3 GF4 MGDF
    1 0.9403 0.9433 0.9681 0.9562 0.9852
    2 0.9787 0.9464 0.9805 0.9908 0.9993
    3 0.9693 0.9712 0.9485 0.9824 0.9989
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-05-19
  • 修回日期:  2022-07-11
  • 刊出日期:  2022-12-20

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