Volume 48 Issue 5
May  2022
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Article Navigation >  Journal of Mine Automation  > 2022  >  48(5): 15-20
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
shu

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

doi: 10.13272/j.issn.1671-251x.2022020024
  • 1.

    College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

  • 2.

    CCTEG Changzhou Research Institute, Changzhou 213015, China

  • 3.

    Tiandi(Changzhou) Automation Co., Ltd., Changzhou 213015, China

  • Received Date: 2022-02-14
  • Rev Recd Date: 2022-05-08
    • Available Online: 2022-03-08
    Abstract
  • Through the analysis and processing of monitoring data, the prognostics health management (PHM) technology for mine inverter power devices can extract signal characteristics, locate the open circuit fault position of power devices, predict the life of power devices and improve the safety and reliability of mine inverter. This paper introduces the principle and research status of signal characteristics extraction method in PHM technology, including coordinate transformation method, spectrum analysis method, wavelet analysis method, empirical mode decomposition method. This paper introduces the principle and research status of power device open circuit fault diagnosis method in PHM technology, including state estimation method, neural network method, support vector machine method. This paper introduces the principle and research status of power device life prediction method in PHM technology, including analytical model method, physical model method, data-driven method. The above methods are compared from five aspects, including implementation difficulty, timeliness, immunity, accuracy and data demand. The signal characteristic extraction method is single. There is open-circuit fault of multiple power devices of mine inverter. The life prediction of power devices based on data-driven method fails to consider the variable working conditions of inverter. In order to solve the above problems, the research directions of PHM technology for mine inverter power devices are proposed. The directions include signal characteristic extraction based on multi-method fusion, open-circuit fault diagnosis of multiple power devices based on intelligent algorithm, fault-tolerant control and health management, and power device life prediction under variable working conditions.

     

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    • References(28)
  • [1]
    王重华. 基于B/S多层结构的煤矿EAM系统[J]. 煤炭技术,2008,27(9):165-166.

    WANG Chonghua. Coal mine EAM system based on B/S multi-layer structure[J]. Coal Technology,2008,27(9):165-166.
    [2]
    曹现刚,段欣宇,张梦园,等. 煤矿设备状态监测系统设计[J]. 工矿自动化,2021,47(5):101-105.

    CAO Xiangang,DUAN Xinyu,ZHANG Mengyuan,et al. Coal mine equipment condition monitoring system design[J]. Industry and Mine Automation,2021,47(5):101-105.
    [3]
    卢东贵,荣相,曾苛. 矿用高压防爆开关微机保护装置现状与展望[J]. 工矿自动化,2018,44(8):28-31.

    LU Donggui,RONG Xiang,ZENG Ke. Status and prospect of microcomputer protection device for mine-used high-voltage explosion-proof switch[J]. Industry and Mine Automation,2018,44(8):28-31.
    [4]
    姜斌,吴云凯,陆宁云,等. 高速列车牵引系统故障诊断与预测技术综述[J]. 控制与决策,2018,33(5):841-855.

    JIANG Bin,WU Yunkai,LU Ningyun,et al. Review of fault diagnosis and prediction technology for high-speed train traction system[J]. Control and Decision,2018,33(5):841-855.
    [5]
    曹现刚,马宏伟,段雍,等. 煤矿设备智能维护与健康管理技术研究现状与展望[J]. 智能矿山,2020,1(1):105-111.

    CAO Xiangang,MA Hongwei,DUAN Yong,et al. Research status and prospect of intelligent maintenance and health management technology for coal mine equipment[J]. Journal of Intelligent Mine,2020,1(1):105-111.
    [6]
    王国法,刘峰,庞义辉,等. 煤矿智能化−煤炭工业高质量发展的核心技术支撑[J]. 煤炭学报,2019,44(2):349-357.

    WANG Guofa,LIU Feng,PANG Yihui,et al. Coal mine intellectualization:the core technology of high quality development[J]. Journal of China Coal Society,2019,44(2):349-357.
    [7]
    王国法,王虹,任怀伟,等. 智慧煤矿2025情景目标和发展路径[J]. 煤炭学报,2018,43(2):295-305.

    WANG Guofa,WANG Hong,REN Huaiwei,et al. 2025 scenario and development path of intelligent coal mine[J]. Journal of China Coal Society,2018,43(2):295-305.
    [8]
    吴少杰. 基于数据挖掘的煤矿机电设备运行状态预测方法研究[D]. 西安: 西安科技大学, 2019.

    WU Shaojie. Research on prediction method of coal mine mechanical and electrical equipment operation status based on data mining[D]. Xi'an: Xi'an University of Science and Technology, 2019.
    [9]
    潘红光,裴嘉宝,侯媛彬. 智慧煤矿数据驱动检测技术研究[J]. 工矿自动化,2020,46(10):49-54.

    PAN Hongguang,PEI Jiabao,HOU Yuanbin. Research on data-driven detection technology of smart coal mine[J]. Industry and Mine Automation,2020,46(10):49-54.
    [10]
    KIM Y J,KIM S H,KIM S M,et al. Open fault diagnosis and tolerance control for grid-connected hybrid active neutral-point-clamped inverters with optimized carrier-based pulse width modulation[J]. IEEE Access,2020,8:145542-145551. doi: 10.1109/ACCESS.2020.3013566
    [11]
    任晓红,万红,俞啸,等. 基于Park变换的三电平逆变器开路故障诊断[J]. 工矿自动化,2020,46(5):82-86,93.

    REN Xiaohong,WAN Hong,YU Xiao,et al. Open-circuit fault diagnosis of three-level inverter based on Park transformation[J]. Industry and Mine Automation,2020,46(5):82-86,93.
    [12]
    ZHANG Jianjian,CHEN Yong,CHEN Zhangyong,et al. Open-switch fault diagnosis method in voltage-source inverters based on phase currents[J]. IEEE Access,2019,7:63619-63625. doi: 10.1109/ACCESS.2019.2913164
    [13]
    张宝伟,帕孜来·马合木提,王芳. 电动汽车逆变器故障诊断[J]. 电力电子技术,2018,52(2):66-68.

    ZHANG Baowei,MAHEMUTI Pazilai,WANG Fang. Fault diagnosis of electrical vehicle inverter[J]. Power Electronics,2018,52(2):66-68.
    [14]
    周超超. 光伏发电系统关键部件的故障诊断方法研究[D]. 无锡: 江南大学, 2018.

    ZHOU Chaochao. Research on fault diagnosis method for key components of photovoltaic power generation system[D]. Wuxi: Jiangnan University, 2018.
    [15]
    李兵,崔介兵,何怡刚,等. 基于能量谱熵及小波神经网络的有源中性点钳位三电平逆变器故障诊断[J]. 电工技术学报,2020,35(10):2216-2225.

    LI Bing,CUI Jiebing,HE Yigang,et al. Fault diagnosis of active neutral point clamped three-level inverter based on energy spectrum entropy and wavelet neural network[J]. Transactions of China Electrotechnical Society,2020,35(10):2216-2225.
    [16]
    吴传龙,陈伟,刘晓文,等. 基于特征融合的提升机逆变器故障诊断[J]. 工矿自动化,2021,47(5):46-51.

    WU Chuanlong,CHEN Wei,LIU Xiaowen,et al. Feature fusion based fault diagnosis of hoist inverter[J]. Industry and Mine Automation,2021,47(5):46-51.
    [17]
    谢东,葛兴来. 基于残差变化率的单相级联H桥整流器IGBT开路故障诊断[J]. 电工技术学报,2018,33(16):3822-3834.

    XIE Dong,GE Xinglai. Residual-changing-rate based open-circuit fault diagnosis for a single-phase cascaded H-bridge rectifier[J]. Transactions of China Electrotechnical Society,2018,33(16):3822-3834.
    [18]
    彭伟发,黄苏融. 永磁同步电机驱动系统逆变器故障诊断研究[J]. 太阳能学报,2019,40(7):1965-1971.

    PENG Weifa,HUANG Surong. Study on fault diagnosis of inverters in PMSM drive system[J]. Acta Energiae Solaris Sinica,2019,40(7):1965-1971.
    [19]
    韩素敏,周孟,郑书晴. 基于BP神经网络的三相电压源型逆变器开路故障诊断[J]. 河南理工大学学报(自然科学版),2021,40(6):126-131,188.

    HAN Sumin,ZHOU Meng,ZHENG Shuqing. Open circuit fault diagnosis of three-phase voltage source inverter based on BP neural network[J]. Journal of Henan Polytechnic University(Natural Science),2021,40(6):126-131,188.
    [20]
    余运俊,裴石磊,万晓凤,等. NPC三电平光伏逆变器开路故障诊断[J]. 电子测量与仪器学报,2018,32(5):41-49.

    YU Yunjun,PEI Shilei,WAN Xiaofeng,et al. Open-circuit fault diagnosis of NPC three-level photovoltaic inverter[J]. Journal of Electronic Measurement and Instrumentation,2018,32(5):41-49.
    [21]
    高镜. 逆变器电流谐波在线监测与故障分析研究[D]. 合肥: 合肥工业大学, 2018.

    GAO Jing. Research on on-line monitoring and fault analysis of inverter current harmonic[D]. Hefei: Hefei University of Technology, 2018.
    [22]
    蒲亚博,王艳艳,赵方超,等. 典型制导弹药故障预示预警系统的设计[J]. 兵器装备工程学报,2021,42(8):39-45. doi: 10.11809/bqzbgcxb2021.08.007

    PU Yabo,WANG Yanyan,ZHAO Fangchao,et al. Design of fault prediction and early warning system for typical guided amunition[J]. Journal of Ordnance Equipment Engineering,2021,42(8):39-45. doi: 10.11809/bqzbgcxb2021.08.007
    [23]
    刘嘉诚. 基于机器学习算法的IGBT寿命预测研究[D]. 合肥: 合肥工业大学, 2020.

    LIU Jiacheng. IGBT life prediction based on machine learning algorithm[D]. Hefei: Hefei University of Technology, 2020.
    [24]
    刘子英,朱琛磊. 基于Elman神经网络模型的IGBT寿命预测[J]. 半导体技术,2019,44(5):395-400.

    LIU Ziying,ZHU Chenlei. IGBT life prediction based on Elman neural network model[J]. Semiconductor Technology,2019,44(5):395-400.
    [25]
    史业照, 郭斌, 郑永军. 基于LSTM网络的IGBT寿命预测研究[J/OL]. 中国测试: 1-6[2021-12-08]. http://kns.cnki.net/kcms/detail/51.1714.TB.20200810.1312.006.html.

    SHI Yezhao, GUO Bin, ZHENG Yongjun. IGBT life prediction based on LSTM network[J/OL]. China Measurement & Test: 1-6[2021-12-08]. http://kns.cnki.net/kcms/detail/51.1714.TB.20200810.1312.006.html.
    [26]
    韩恒贵. 基于深度学习的IGBT故障预测研究[D]. 北京: 北京交通大学, 2019.

    HAN Henggui. Research on IGBT fault prediction based on deep learning[D]. Beijing: Beijing Jiaotong University, 2019.
    [27]
    孙梓涵. 基于数据驱动的IGBT寿命预测研究[D]. 北京: 北京交通大学, 2020.

    SUN Zihan. Research on IGBT life prediction based on data drive[D]. Beijing: Beijing Jiaotong University, 2020.
    [28]
    葛建文,黄亦翔,陶智宇,等. 基于Transformer模型的IGBT剩余寿命预测[J]. 半导体技术,2021,46(4):316-323.

    GE Jianwen,HUANG Yixiang,TAO Zhiyu,et al. Residual useful life prediction of IGBTs based on Transformer model[J]. Semiconductor Technology,2021,46(4):316-323.
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