|本期目录/Table of Contents|

[1]张佳佳,高军伟.基于IBA-LSSVM的轨道交通辅助逆变器故障诊断[J].工业仪表与自动化装置,2022,(02):86-90.[doi:10.19950/j.cnki.cn61-1121/th.2022.02.018]
　ZHANG Jia-jia,GAO Jun-wei.Fault diagnosis of rail transit auxiliary inverter based on IBA-LSSVM[J].Industrial Instrumentation & Automation,2022,(02):86-90.[doi:10.19950/j.cnki.cn61-1121/th.2022.02.018]
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基于IBA-LSSVM的轨道交通辅助逆变器故障诊断

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2022年02期
页码:
86-90
栏目:
出版日期:
2022-04-15

文章信息/Info

Title:
Fault diagnosis of rail transit auxiliary inverter based on IBA-LSSVM
文章编号:
1000-0682（2022）02-0000-00
作者:
张佳佳1; 2; 高军伟1; 2
（1.青岛大学 自动化学院；2.青岛大学 山东省工业控制技术重点实验室，山东 青岛 266071）
Author(s):
ZHANG Jia-jia1; 2;  GAO Jun-wei1; 2
(1.School of Automation, Qingdao University; 2. Shandong Province Key Laboratory of Industrial Control Technology, Qingdao University,Shandong Qingdao 266071,China)
关键词:
辅助逆变器; 故障诊断; LSSVM; 改进蝙蝠算法
Keywords:
auxiliary Inverter; fault diagnosis; LSSVM; improved bat algorithm
分类号:
TP206+.3
DOI:
10.19950/j.cnki.cn61-1121/th.2022.02.018
文献标志码:
A
摘要:
为降低轨道交通列车辅助逆变器(SIV)中三相逆变桥IGBT开路故障的排查难度和保障列车安全运行,对常见的多种IGBT故障进行诊断。提出了改进的完全自适应噪声集合经验模态分解方法(ICEEMDAN)、改进的蝙蝠算法（IBA）优化最小二乘支持向量机（LSSVM)模型参数相结合的故障诊断方法，来改善特征提取效果和诊断精度。通过MATLAB仿真实验可以看出，相较于蝙蝠算法（BA）优化LSSVM,粒子群算法（PSO）优化LSSVM，IBA- LSSVM模型在诊断速度和精度上都具有很大优势。
Abstract:
In order to reduce the difficulty of troubleshooting IGBT open circuit faults of the three-phase inverter bridge in the auxiliary inverter (SIV) of rail transit trains and to ensure the safe operation of trains, a variety of common IGBT faults are diagnosed. A fault diagnosis method that combines an improved fully adaptive noise ensemble empirical mode decomposition method (ICEEMDAN) and an improved bat algorithm (IBA) to optimize the least square support vector machine (LSSVM) model parameters is proposed to improve the feature extraction effect and Diagnostic accuracy. Through MATLAB simulation experiments, it can be seen that compared to Bat Algorithm (BA) optimizing LSSVM and Particle Swarm Algorithm (PSO) optimizing LSSVM, IBA-LSSVM model has great advantages in diagnosis speed and accuracy.

参考文献/References:

[1]翁晓韬,冯珏,刘中良,等.城市轨道交通车辆辅助逆变器离线式诊断系统设计与应用[J].城市轨道交通研究,2020,23(06):132-134+138.

[2]牟文博.地铁车辆辅助逆变器工作原理及典型故障分析[J].轨道交通装备与技术,2020(02):30-33.
[3]盖宏宇,高军伟,种兴静.基于MEEMD和GRNN的轨道交通列车辅助逆变器故障诊断[J].制造业自动化,2019,41(11):11-14.
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备注/Memo

备注/Memo:
收稿日期：2021-12-11

基金项目：
山东省自然科学基金资助项目（ZR2019MF063）；
山东省重点研发计划（2017GGX10115）

作者简介:
张佳佳(1996)，女，土家族，湖南湘西人，硕士研究生，主要研究方向为智能系统与智能控制。
更新日期/Last Update: 1900-01-01