Failure Mode Classification of IGBT Modules Under Power Cycling Tests Based on Data-Driven Machine Learning Framework
Author:
Affiliation:
1. College of Electrical and Information Engineering, Hunan University, Changsha, China
2. CRRC Zhuzhou Electric Locomotive Institute Company Ltd., Zhuzhou, China
Funder
National Natural Science Foundation of China
Natural Science Foundation of Hainan Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/63/10297410/10247579.pdf?arnumber=10247579
Reference69 articles.
1. In Situ Diagnostics and Prognostics of Solder Fatigue in IGBT Modules for Electric Vehicle Drives
2. A Lifetime Prediction Method for IGBT Modules Considering the Self-Accelerating Effect of Bond Wire Damage
3. Prediction of Bond Wire Fatigue of IGBTs in a PV Inverter under a Long-Term Operation
4. Lifetime Prediction of IGBT Modules in Suspension Choppers of Medium/Low-Speed Maglev Train Using an Energy-Based Approach
5. A Comprehensive Review Toward the State-of-the-Art in Failure and Lifetime Predictions of Power Electronic Devices
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