Research on Fault Diagnosis of Track Circuit Based on BFOA-PSO-GMM

被引：0

作者：

Sun B. ^{[1
]}

Zhao M. ^{[1
]}

He H. ^{[2
]}

机构：

[1] College of Electronic and Information Engineering, Shandong University of Seienee and Technology, Cungdao

[2] Hunan Huahuite Automation Technology Co., Ltd., Changsha

来源：

Tiedao Xuebao/Journal of the China Railway Society | 2024年 / 46卷 / 05期

关键词：

bacterial foraging optimization algorithm; fault diagnosis; Gaussian mixed model; particle swarm optimization algorithm; track circuit;

D O I：

10.3969/j.issn.1001-8360.2024.05.010

中图分类号：

学科分类号：

摘要：

In view of the huge track circuit system and various types of faults, this paper proposed a Gaussian mixture model that combines bacterial foraging optimization algorithm and particle swarm optimization algorithm to diagnose multiple failure types of the track circuit. By fusing bacterial foraging optimization algorithm and particle swarm optimization algorithm to find the initial value suitable for the EM algorithm, the model effectively avoided the EM algorithm from falling into local optima, resulting in the improvement of its fault diagnosis ability. Through the training and testing experiments on the measured data, it is shown that the fault diagnosis accuracy of the model is 31. 85% higher than that of the original Gaussian mixed model, and 9. 4% higher than the fault diagnosis accuracy of the improved model using the particle swarm optimization algorithm, proving that the model is more effective in fault diagnosis of track circuits. © 2024 Science Press. All rights reserved.

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页码：85 / 91

页数：6

共 14 条

[11] CHEN Yuanfan, Shunming LI, Bearing Fault Diagnosis Based on Gauss Mixture Model and the Improved Grid Search Method[J], Journal of Chongqing University of Technology (Natural Science), 30, 3, pp. 34-39, (2016)
[12] WEI Yi, ZHANG Yuewen, LI Bin, Fault Diagnosis of Ship Fuel System Based on DPC-GMM Algorithm, Chinese Journal of Ship Research, 13, 6, pp. 147-153, (2018)
[13] ZHANG Dingxue, LIAO Ruiquan, Adaptive Particle Swarm Optimization Algorithm Based on Population Velocity, Control and Decision, 24, 8, pp. 1257-1260, (2009)
[14] LIANG Yingxin, TIAN Haoshan, Improved Hybrid Algorithm Based on Bacterial Foraging and Particle Swarm Optimization J], Electronic Science and Technology, 30, 4, pp. 79-82, (2017)

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