An Aging-Degree Evaluation Method for IGBT Bond Wire with Online Multivariate Monitoring

Zilang Hu, Xinglai Ge, Dong Xie, Yichi Zhang, Bo Yao, Jian Dai and Fengbo Yang
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Zilang Hu: National Rail Transportation Electrification and Automation Engineering Technology Research Center, Southwest Jiaotong University, Chengdu 611756, China
Xinglai Ge: National Rail Transportation Electrification and Automation Engineering Technology Research Center, Southwest Jiaotong University, Chengdu 611756, China
Dong Xie: National Rail Transportation Electrification and Automation Engineering Technology Research Center, Southwest Jiaotong University, Chengdu 611756, China
Yichi Zhang: National Rail Transportation Electrification and Automation Engineering Technology Research Center, Southwest Jiaotong University, Chengdu 611756, China
Bo Yao: National Rail Transportation Electrification and Automation Engineering Technology Research Center, Southwest Jiaotong University, Chengdu 611756, China
Jian Dai: National Rail Transportation Electrification and Automation Engineering Technology Research Center, Southwest Jiaotong University, Chengdu 611756, China
Fengbo Yang: National Rail Transportation Electrification and Automation Engineering Technology Research Center, Southwest Jiaotong University, Chengdu 611756, China

Energies, 2019, vol. 12, issue 20, 1-18

Abstract: The aging fracture of bonding wire is one of the main reasons for failure of insulated gate bipolar transistor (IGBT). This paper proposes an online monitoring method for IGBT bonding wire aging that does not interfere with the normal operation of the IGBT module. A quantitative analysis of aging degree was first performed, and the results of multivariate and univariate monitoring were compared. Based on the relationship between the monitoring parameters and the aging of the IGBT bonding wire, gradual damage of the IGBT bond wire was implemented to simulate aging failure and obtain the aging data. Moreover, the change of junction temperature was considered to regulate monitoring parameters. Then, the aging degree was evaluated by an artificial neural network (ANN) algorithm. The experimental results showed the effectiveness of the proposed method.

Keywords: IGBT bond wire; aging-degree evaluation; online multivariate monitoring; neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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