Classification Method for Mechanical Defects in GIS Equipment Based on Mode Function Analysis and Improved Relevance Vector Machines

Yao Zhong; Jian Hao; Xupeng Wang; Ruijin Liao; Ruilei Gong; Yilin Ding

doi:10.17775/CSEEJPES.2021.03580

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Classification Method for Mechanical Defects in GIS Equipment Based on Mode Function Analysis and Improved Relevance Vector Machines

Regular Papers | 更新时间：2026-10-15

- Classification Method for Mechanical Defects in GIS Equipment Based on Mode Function Analysis and Improved Relevance Vector Machines
- Classification Method for Mechanical Defects in GIS Equipment Based on Mode Function Analysis and Improved Relevance Vector Machines
- 中国电机工程学会电力与能源系统学报(英文) 2023年9卷第2期页码：790-801
- 作者机构：
  
  1. State Key Laboratory of Power Transmission Equipment & System Security and New Technology, University of Chongqing,Chongqing,China
  2. Shandong Taikai High Voltage Swichgear Co. Ltd., Taian,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2021.03580
  中图分类号：
- 纸质出版：2023-03-01
- 稿件说明：
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Yao Zhong, Jian Hao, Xupeng Wang, 等. Classification Method for Mechanical Defects in GIS Equipment Based on Mode Function Analysis and Improved Relevance Vector Machines[J]. 中国电机工程学会电力与能源系统学报(英文), 2023,9(2):790-801.

Yao Zhong, Jian Hao, Xupeng Wang, et al. Classification Method for Mechanical Defects in GIS Equipment Based on Mode Function Analysis and Improved Relevance Vector Machines[J]. CSEE Journal of Power and Energy Systems, 2023, 9(2): 790-801.
Yao Zhong, Jian Hao, Xupeng Wang, 等. Classification Method for Mechanical Defects in GIS Equipment Based on Mode Function Analysis and Improved Relevance Vector Machines[J]. 中国电机工程学会电力与能源系统学报(英文), 2023,9(2):790-801. DOI： 10.17775/CSEEJPES.2021.03580.

Yao Zhong, Jian Hao, Xupeng Wang, et al. Classification Method for Mechanical Defects in GIS Equipment Based on Mode Function Analysis and Improved Relevance Vector Machines[J]. CSEE Journal of Power and Energy Systems, 2023, 9(2): 790-801. DOI： 10.17775/CSEEJPES.2021.03580.

摘要

Abstract

Mechanical defects

in gas-insulated switchgear (GIS) equipment

have weak response characteristics

leading to significant difficulties in the classification of defects. Therefore

this paper proposes a novel mechanical defect feature extraction and classification method that combines independent intrinsic mode function (IIMF) analysis and an improved multi-kernel mapping fast multi-classification relevance vector machine (MKF -mRVM). Enlightened by the differences in the GIS operating vibration mode

the IIMF series were first obtained based on regenerated phase-shifted sinusoid-assisted empirical mode decomposition (RPSEMD) and modal judgments. Then singular value decomposition and time-frequency conversions were performed to construct combined feature matrices. Finally

multikernel mapping and domain sampling were introduced to improve the calculation speed and recognition accuracy of the mRVM

which was more suitable for on-line monitoring. Results show that the proposed RPSEMD-MKF-mRVM model achieves a faster training speed (14.23 s) and higher accuracy (98.21%) than other algorithms

and it can adapt to variable loads.

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