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矿用逆变器功率器件故障预测与健康管理技术现状及展望

李红岩 杨朝旭 荣相 史晗 王越 刘宝 王磊

李红岩,杨朝旭,荣相,等. 矿用逆变器功率器件故障预测与健康管理技术现状及展望[J]. 工矿自动化,2022,48(5):15-20.  doi: 10.13272/j.issn.1671-251x.2022020024
引用本文: 李红岩,杨朝旭,荣相,等. 矿用逆变器功率器件故障预测与健康管理技术现状及展望[J]. 工矿自动化,2022,48(5):15-20.  doi: 10.13272/j.issn.1671-251x.2022020024
LI Hongyan, YANG Chaoxu, RONG Xiang, et al. Research status and prospect of prognostics health management technology for mine inverter power devices[J]. Journal of Mine Automation,2022,48(5):15-20.  doi: 10.13272/j.issn.1671-251x.2022020024
Citation: LI Hongyan, YANG Chaoxu, RONG Xiang, et al. Research status and prospect of prognostics health management technology for mine inverter power devices[J]. Journal of Mine Automation,2022,48(5):15-20.  doi: 10.13272/j.issn.1671-251x.2022020024

矿用逆变器功率器件故障预测与健康管理技术现状及展望

doi: 10.13272/j.issn.1671-251x.2022020024
基金项目: 国家自然科学基金资助项目(61703329);天地(常州)自动化股份有限公司研发项目(2021GY1003);天地科技股份有限公司科技创新创业资金专项资助项目(2020-2-TD-CXY003);陕西省重点研发计划项目(2019GY-097)。
详细信息
    作者简介:

    李红岩(1980—),男,山东东阿人,高级工程师,博士,主要研究方向为电动机与电器智能检测及故障诊断,E-mail:lihongyan@xust.edu.cn

    通讯作者:

    杨朝旭(1996—),男,陕西汉中人,硕士研究生,主要研究方向为功率器件故障诊断及寿命预测,E-mail:987486389@qq.com

  • 中图分类号: TD67

Research status and prospect of prognostics health management technology for mine inverter power devices

  • 摘要: 矿用逆变器功率器件故障预测与健康管理(PHM)技术通过对监测数据分析处理,能够提取信号特征、定位功率器件开路故障位置、预测功率器件寿命,提高矿用逆变器安全性和可靠性。详细介绍了PHM技术中信号特征提取方法(主要包括坐标变换法、频谱分析法、小波分析法、经验模态分解法)、功率器件开路故障诊断方法(主要包括状态估计法、神经网络法、支持向量机法)、功率器件寿命预测方法(主要包括解析模型法、物理模型法、数据驱动法)的原理及研究现状。分别从实现难度、时效、抗扰性、准确度和数据需求量5个方面对上述各方法进行了比较。针对目前信号特征提取方法单一、矿用逆变器多功率器件开路故障、基于数据驱动法的功率器件寿命预测未能考虑逆变器变工况条件等问题,提出了矿用逆变器功率器件PHM技术的研究方向,包括多方法融合的信号特征提取、基于智能算法的多功率器件开路故障诊断、容错控制和健康管理、变工况下功率器件寿命预测。

     

  • 图  1  矿用逆变器功率器件PHM技术构架

    Figure  1.  Prognostics health management technical framework of mine inverter power device

    图  2  基于小波分析法的信号特征提取流程

    Figure  2.  Signal characteristic extraction process based on wavelet analysis method

    图  3  基于状态观测器的逆变器功率器件开路故障诊断原理

    Figure  3.  Principle of open-circuit fault diagnosis of inverter power device based on state observer

    表  1  信号特征提取方法比较

    Table  1.   Comparison of signal characteristic extraction methods

    信号特征提取方法实现难度时效抗扰性准确度数据需求量
    坐标变换法
    频谱分析法
    小波分析法
    模态分解法
    下载: 导出CSV

    表  2  功率器件开路故障诊断方法比较

    Table  2.   Comparison of open-circuit fault diagnosis methods for power device

    功率器件开路
    故障诊断方法
    实现难度时效抗扰性准确度数据需求量
    状态估计法
    神经网络法
    支持向量机法
    下载: 导出CSV

    表  3  功率器件寿命预测方法比较

    Table  3.   Comparison of power device life prediction methods

    功率器件寿命
    预测方法
    实现难度时效抗扰性准确度数据需求量
    解析模型法
    物理模型法
    数据驱动法
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-02-14
  • 修回日期:  2022-05-08
  • 网络出版日期:  2022-03-08

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