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基于改进深度森林的采煤机拖拽电缆挤压力识别方法

石港 雷志鹏

石港,雷志鹏. 基于改进深度森林的采煤机拖拽电缆挤压力识别方法[J]. 工矿自动化,2023,49(10):8-16, 51.  doi: 10.13272/j.issn.1671-251x.2023050042
引用本文: 石港,雷志鹏. 基于改进深度森林的采煤机拖拽电缆挤压力识别方法[J]. 工矿自动化,2023,49(10):8-16, 51.  doi: 10.13272/j.issn.1671-251x.2023050042
SHI Gang, LEI Zhipeng. Recognition method of the squeezing force of shearer dragging cable based on improved deep forest[J]. Journal of Mine Automation,2023,49(10):8-16, 51.  doi: 10.13272/j.issn.1671-251x.2023050042
Citation: SHI Gang, LEI Zhipeng. Recognition method of the squeezing force of shearer dragging cable based on improved deep forest[J]. Journal of Mine Automation,2023,49(10):8-16, 51.  doi: 10.13272/j.issn.1671-251x.2023050042

基于改进深度森林的采煤机拖拽电缆挤压力识别方法

doi: 10.13272/j.issn.1671-251x.2023050042
基金项目: 国家自然科学基金资助项目(51977137)。
详细信息
    作者简介:

    石港(1997—),男,山西阳泉人,硕士,主要研究方向为电缆故障局部放电特性与故障诊断,E-mail:sg18735160898@163.com

    通讯作者:

    雷志鹏(1983—),男,山西太原人,副教授,博士,主要从事矿用智能电器和电气绝缘测试方面的研究工作,E-mail:leizhipeng@163.com

  • 中图分类号: TD611

Recognition method of the squeezing force of shearer dragging cable based on improved deep forest

  • 摘要: 采煤机拖拽电缆在运行中常受到外部挤压力作用,致使电缆绝缘发生局部放电,影响电缆使用寿命。现有研究侧重于局部放电规律和严重程度的分析,无法评估乙丙橡胶绝缘电缆所承受应力的大小,导致无法掌握矿用乙丙橡胶绝缘电缆的运行状态。针对该问题,提出一种基于改进深度森林(S−DF)的采煤机拖拽电缆挤压力识别方法。通过实验测量了不同挤压力下采煤机拖拽电缆的局部放电,分析了局部放电谱图、平均放电电流、最大放电量和击穿场强随所施挤压力和电压的变化规律,计算了局部放电的统计特征参量。基于统计特征参量,采用S−DF模型对挤压力大小进行识别。S−DF模型在深度森林(DF)中引入Stacking集成算法,以提升识别准确率。研究结果表明:不同电压下,最大放电量和平均放电电流均随着挤压力的增大而减小;击穿场强随着挤压力的增大呈先增大后减小的趋势,挤压力大于2 000 N时的击穿场强小于未挤压时的击穿场强;不同挤压力下的局部放电统计特征参量可作为放电指纹,S−DF模型能准确地识别电缆所受挤压力的大小,且识别率高于其他传统分类算法。

     

  • 图  1  局部放电测量装置原理

    Figure  1.  Principle of partial discharge measurement device

    图  2  介电强度实验电路

    Figure  2.  Dielectric strength experimental circuit

    图  3  不同挤压力下乙丙橡胶绝缘电缆的PRPD图

    Figure  3.  PRPD diagram of ethylene propylene rubber cable under different extrusion pressures

    图  4  击穿场强与挤压力的关系

    Figure  4.  Relationship between puncture core field intensity and extrusion pressure

    图  5  最大放电量和平均放电电流与挤压力的关系

    Figure  5.  Relationship between maximum discharge, average discharge current and extrusion pressure

    图  6  不同挤压力下的统计特征参量柱状图

    Figure  6.  Histograms of statistical characteristic parameters under different extrusion pressures

    图  7  Stacking集成算法学习过程

    Figure  7.  Learning process of Stacking ensemble algorithm

    图  8  多粒度扫描原理

    Figure  8.  Principle of multi granularity scanning

    图  9  级联森林结构原理

    Figure  9.  Principle of cascading forest structure

    图  10  S−DF结构

    Figure  10.  Structure of Stacking-deep forest

    图  11  训练次数对准确率的影响

    Figure  11.  The effect of training times on accuracy

    表  1  PRPD图统计特征参量

    Table  1.   Statistical characteristic parameters of PRPD diagram

    参数HqmaxφHqnφHnφHnq
    +++
    Sk
    Ku
    Peak
    Assy
    Cc
    下载: 导出CSV

    表  2  S−DF模型与DF模型识别准确率对比

    Table  2.   Comparison of recognition accuracy between S-DF model and DF model

    训练集与
    验证集之比
    识别准确率/%
    S−DFDF
    0.8∶0.294.2787.65
    0.5∶0.593.5586.85
    0.2∶0.888.6580.95
    下载: 导出CSV

    表  3  S−DF模型与随机森林、SVM识别准确率对比

    Table  3.   Comparison of recognition accuracy between S-DF model and random forest and SVM

    挤压力/N 识别准确率/%
    S−DF 随机森林 SVM
    0 85.20 87.54 85.52
    500 96.20 73.42 76.72
    1 000 95.60 78.12 74.73
    1 500 97.40 65.00 70.52
    2 000 96.20 85.83 86.73
    2 500 95.20 81.33 88.45
    平均值 94.27 78.54 80.46
    下载: 导出CSV
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  • 收稿日期:  2023-05-11
  • 修回日期:  2023-10-19
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