|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]
点击复制

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12高军伟12
(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-jia12 GAO Jun-wei12
(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 Inverterfault diagnosisLSSVMimproved 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.
[4]吴浩,王泉,王睿轶.基于IGBT特征分析的地铁车辆逆变器故障研究[J].城市轨道交通研究,2020,23(09):51-55.?div>[5]蔡鹏飞,郑树彬,彭乐乐.基于EEMD和GA-BP的列车辅助逆变器开路故障诊断研究[J].计算机测量与控制,2019,27(07):25-31.
[6]朱敏,段志善,郭保良.CEEMDAN结合排列熵在滚动轴承故障特征分析中的应用[J].现代制造工程,2019(04):151-154+74.
[7]赵磊,张永祥,朱丹宸.基于TVD和ICEEMDAN的滚动轴承微弱故障特征提取[J].海军工程大学学报,2020,32(04):61-66.?/div>
[8]彭铎,汪昕,王伟治.改进的蝙蝠算法及其在WSNs定位中的应用[J].传感器与微系统,2020,39(07):157-160.?div>[9]陈强,解成能,刑继刚,等.一种改进的花朵授粉优化算法[J].机电工程技术,2020,49(07):211-213+224.
[10]邱英强,吴京龙,陈俊,等.基于机器学习算法的风电机组故障预测系统设计[J].自动化与仪器仪表,2021(09):190-193.
[11]刘金燕,王冬青,崔建伟.基于改进核LS-SVM算法的螺丝锁附结果分类研究[J].工业仪表与自动化装置,2020(04):12-15+24.
[12]季颖,李小波,冯鹏飞,等.城轨车辆辅助逆变器IGBT状态识别研究[J].计算机测量与控制,2018,26(06):152-154+159.

相似文献/References:

[1]尹新权,王 珺,张亚萍.基于模糊理论的柴油机故障诊断专家系统[J].工业仪表与自动化装置,2015,(01):111.
 YIN Xinquan,WANG Jun,ZHANG Yaping.Fault diagnostic expert system of diesel engine based on fuzzy theory[J].Industrial Instrumentation & Automation,2015,(02):111.
[2]孟文俊a,徐光华a,b,等.基于LabVIEW的滚动轴承非平稳过程监测诊断及性能评估系统的开发[J].工业仪表与自动化装置,2015,(02):18.
 MENG Wenjuna,XU Guanghuaa,b,et al.Development of non-stationary process for rolling bearing fault diagnosis and performance evaluation system based on LabVIEW[J].Industrial Instrumentation & Automation,2015,(02):18.
[3]李 茜,王延年.基于普通铣床数控化的S7-300 PLC远程监控和故障诊断系统设计[J].工业仪表与自动化装置,2015,(02):49.
 LI Qian,WANG Yannian.Design of remote monitoring and fault diagnosis systembased on the ordinary milling machine of numerical control of S7-300 PLC[J].Industrial Instrumentation & Automation,2015,(02):49.
[4]巴寅亮,王书提,谢 鑫.基于改进的BP神经网络的柴油发动机故障诊断[J].工业仪表与自动化装置,2015,(03):94.
 BA Yinliang,WANG Shuti,XIE Xin.Research of diesel engine fault based on improved BP neural network[J].Industrial Instrumentation & Automation,2015,(02):94.
[5]王江荣,文 晖,黄建华.基于差分进化算法的二次回归在矿井通风机故障诊断中的应用[J].工业仪表与自动化装置,2015,(01):50.
 WANG Jiangrong,WEN Hui,HUANG Jianhua.The two regression in ventilator fault diagnosis application based on difference evolutionary algorithm[J].Industrial Instrumentation & Automation,2015,(02):50.
[6]张卫峰,惠俊军.智能故障诊断技术的现状及展望[J].工业仪表与自动化装置,2017,(05):21.
 ZHANG Weifeng,HUI Junjun.The present situation and prospects of intelligence fault diagnosis technology[J].Industrial Instrumentation & Automation,2017,(02):21.
[7]宫玮丽,梁 波,王晓兰.基于小波包和Hilbert包络分析的隧道掘进机主轴承故障诊断方法研究[J].工业仪表与自动化装置,2018,(02):15.[doi:1000-0682(2018)02-0000-00]
 GONG Weili,LIANG Bo,WANG Xiaolan.Research on fault diagnosis method of main bearing of tunnel boring machine based on wavelet packet and Hilbert envelope analysis[J].Industrial Instrumentation & Automation,2018,(02):15.[doi:1000-0682(2018)02-0000-00]
[8]席 维,白 璘,武奇生.基于经验小波变换和峭度值的滚动轴承故障检测方法[J].工业仪表与自动化装置,2018,(06):26.[doi:1000-0682(2018)06-0000-00]
 XI Wei,BAI Lin,WU Qisheng.A novel rolling bearing fault detection method based on empirical wavelet transform and kurtosis value[J].Industrial Instrumentation & Automation,2018,(02):26.[doi:1000-0682(2018)06-0000-00]
[9]张远绪,程换新.基于改进的RBF神经网络的滚动轴承故障诊断[J].工业仪表与自动化装置,2018,(06):31.[doi:1000-0682(2018)06-0000-00]
 ZHANG Yuanxu,CHENG Huanxin.Fault diagnosis of rolling bearing based on improved RBF neural network[J].Industrial Instrumentation & Automation,2018,(02):31.[doi:1000-0682(2018)06-0000-00]
[10]郭兰中,彭刘阳,窦 岩,等.基于小波包-AR谱和GA-BP网络的轴承故障诊断研究[J].工业仪表与自动化装置,2019,(03):3.[doi:1000-0682(2019)03-0000-00]
 GUO Lanzhong,PENG Liuyang,DOU Yan,et al.Research on bearing fault diagnosis based on wavelet packet –auto regressive model spectrum and GA-BP neural network[J].Industrial Instrumentation & Automation,2019,(02):3.[doi:1000-0682(2019)03-0000-00]

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