Gong-kuang zidonghua (May 2022)

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

  • LI Hongyan,
  • YANG Chaoxu,
  • RONG Xiang,
  • SHI Han,
  • WANG Yue,
  • LIU Bao,
  • WANG Lei

DOI
https://doi.org/10.13272/j.issn.1671-251x.2022020024
Journal volume & issue
Vol. 48, no. 5
pp. 15 – 20

Abstract

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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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