IGBT Module Lifetime Prediction Technology for Rail Transit

Bin WANG; Lin CAO

doi:10.13890/j.issn.1000-128x.2020.01.002

您当前的位置：

首页 >

文章列表页 >

IGBT Module Lifetime Prediction Technology for Rail Transit

Power Semiconductor Technology | 更新时间：2021-12-10

- IGBT Module Lifetime Prediction Technology for Rail Transit
- Electric Drive for Locomotives Issue 1, Pages: 9-12(2020)
- 作者机构：
  
  1.中车永济电机有限公司　研究院，山西　永济　044502
- 作者简介：
- 基金信息：
- DOI：10.13890/j.issn.1000-128x.2020.01.002
  CLC：
扫描看全文
Bin WANG, Lin CAO. IGBT Module Lifetime Prediction Technology for Rail Transit. [J]. Electric Drive for Locomotives (1):9-12(2020)
DOI：

Bin WANG, Lin CAO. IGBT Module Lifetime Prediction Technology for Rail Transit. [J]. Electric Drive for Locomotives (1):9-12(2020) DOI： 10.13890/j.issn.1000-128x.2020.01.002.

摘要

随着中国轨道交通车辆陆续进入高级修阶段，IGBT器件寿命预测技术成为行业关注的热点，科学的寿命评估方法是实现IGBT变流器全生命周期管理的基础。文章针对轨道交通的应用，介绍了IGBT器件失效机理、寿命预测模型及计算流程；通过概述国内外相关研究现状，归纳了目前IGBT器件寿命预测面临的难题，分析了通过采用智能化驱动、大数据分析及人工智能、状态检测等技术来提高IGBT器件寿命预测的准确性。

Abstract

Rail transport IGBT module lifetime prediction technology has become research focuses due to China's rail transit vehicles entering the stage of overhaul, thus scientific module lifetime prediction method is the key to realize the IGBT converter life cycle management. Based on rail transit applications, the failure mechanism, lifetime prediction model and process of IGBT module were introduced, and the challenges by collect relevant research results both at home and abroad were summarized, then it was proposed that IGBT intelligent drive, big data analysis and artificial intelligence, condition monitor and other technologies could improve the accuracy of IGBT module lifetime prediction.

关键词

轨道交通IGBT器件寿命预测仿真

Keywords

railway transitIGBT modulelifetime predictionsimulation

references

丁荣军, 刘国友. 轨道交通用高压IGBT技术特点及发展趋势[J]. 机车电传动, 2014 (1): 1-6.

刘国友, 覃荣震, DEVINY Ian, 等. 牵引用3 300 V IGBT/FRD 芯片组设计与开发[J]. 机车电传动, 2013 (2): 5-8.

覃荣震, 刘国友, 黄建伟. 轨道交通用IGBT模块互连技术及其发展趋势[J]. 机车电传动, 2013 (3): 1-5.

鲁光祝. IGBT功率模块寿命预测技术[D]. 重庆: 重庆大学,2012.

刘宾礼, 刘德志, 唐勇, 等. 基于加速寿命试验的IGBT模块寿命预测和失效分析[J]. 江苏大学学报(自然科学版), 2013, 34(5): 556-563.

张树冰, 刘雪婷. 功率模块IGBT失效机理与寿命预测研究综述[J]. 电气开关, 2017, 55(5): 19-21.

赖伟, 陈民铀, 冉立, 等. 老化实验条件下的IGBT寿命预测模型[J]. 电工技术学报, 2016, 31(24): 173-180.

NAGEL A, QUAST F. Requirements on the load cycling capability of IGBT modules in traction applications[C]//IEEE. 2019 13th European Conference on Power Electronics and Applications. Barcelona: IEEE, 2009: 1-6.

PERPIÑÀ X, NAVARRO L, JORDÀ X, et al. Reliability and lifetime prediction for IGBT modules in railway traction chains[M]//PERPINYA X. Reliability and Safety in Railway. Rijeka: InTech Europe, 2012: 193-222.

BERNER J. Load-cycling capability of HiPak IGBT modules[R]. Lenzburg: ABB Switzerland Ltd, 2012.

王彦刚, DINESH Chamund, 李世平, 等. 功率IGBT 模块的寿命预测[J]. 机车电传动, 2013 (2): 13-17.

DING J, ZHANG P, LI J H. Fatigue life prediction of IGBT module for metro vehicle traction converter based on traction calculation[C]//IEEE. 2015 IEEE 11th International Conference on Power Electronics and Drive Systems. Sydney: IEEE, 2015: 1116-1121. DOI: 10.1109/PEDS.2015.7203578http://doi.org/10.1109/PEDS.2015.7203578.

曹琳, 叶娜, 李萍, 等. 半实物仿真技术在轨道交通用IGBT寿命预测中的应用[J]. 机车电传动, 2019 (5): 59-62.

文阳, 杨媛, 高勇. 数字化IGBT 智能驱动与故障监测存储系统设计[J]. 电气传动, 2016, 46(7): 73-76.

孔梅娟, 李志刚, 李雄, 等. IGBT功率模块状态监测技术研究现状[J]. 半导体技术, 2017, 42(2): 145-152.

Views

下载量

CSCD

CNKI被引量

Alert me when the article has been cited

提交

Tools

Publicity Resources

Fatigue life study of the door on the new-type 160 km/h railway box wagon

Research on suspension control of maglev train via enhanced tracking differentiator

Fault diagnosis technology for three-level inverter based on ICEEMDAN-FE and SVM

Study on energy consumption performance of permanent magnet synchronous traction system for trains on Ningbo metro line 4

Integrated simulation test platform of operation control system for high-speed maglev transport

Related Author

No data

Related Institution

Technology R&D Center, CRRC Braking System Co., Ltd.

School of Mechanical Engineering, Southwest Jiaotong University

School of Mechanical Engineering, Dalian Jiaotong University

Chongqing Technology R&D Center, CRRC SRI Chongqing Intelligent Equipment Technology Co., Ltd.

College of Computer and Data Science, Fuzhou University

⁰