Dynamic inner independent component analysis-based incipient fault detection for electric drive systems of high-speed trains

被引：0

作者：

Wang, Hongmei ^{[1
]}

Wang, Jingkun ^{[1
]}

Xu, Shuiqing ^{[2
]}

Cheng, Chao ^{[1
]}

Liu, Qiang ^{[3
]}

Chen, Hongtian ^{[4
]}

机构：

[1] Changchun Univ Technol, Coll Comp Sci & Engn, Changchun, Peoples R China

[2] Hefei Univ Technol, Coll Elect Engn & Automat, Hefei, Peoples R China

[3] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang, Peoples R China

[4] Shanghai Jiao Tong Univ, Dept Automat, Shanghai, Peoples R China

来源：

JOURNAL OF CONTROL AND DECISION | 2024年 / 11卷 / 03期

关键词：

Dynamic inner independent component analysis; independent component analysis; electric drive systems; fault detection; TRACTION SYSTEMS;

D O I：

10.1080/23307706.2023.2198260

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The operating data of high-speed train electric drive systems contain unknown disturbances and noise, which makes it challenging to identify incipient faults. In order to improve the incipient fault detection capability of the electric drive system, a fault detection algorithm based on dynamic inner independent component analysis is proposed. In this paper, a mathematical proof of the dynamic inner independent component analysis algorithm is first given, and then the method is validated by means of an electric drive system simulation platform. The simulation results show that the dynamic fault detection method proposed in this paper can effectively monitor the operating status of the electric drive system without the need to establish a mathematical model of the system and expertise. Compared with the fault detection methods based on independent component analysis and principal component analysis, the proposed method decreases the fault detection time and reduces the false alarm rate and missing alarm rate.

引用

页码：417 / 427

页数：11

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