A data-driven lifetime prediction method for thermal stress fatigue failure of power MOSFETs. (November 2022)

• Record Type: Journal Article
• Authors: Wang, Xiang; Wei, Weiwei; Zhang, Yanhui; Feng, Wei; Xu, Guoqing; Xiang, An
• Is Part Of: Energy reports. Volume 8(2022)Supplement 15
• Page Start: 467
• Page End: 473
• Publication Date: 2022-11
• Subjects: Power MOSFET -- Lifetime prediction -- On-state resistance -- Data-driven; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.10.137
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: As one of the core power electronic devices that undertake power conversion and control tasks in electrical systems, power MOSFETs are widely used in key fields such as transportation, industrial drives, and aerospace. At present, the traditional method improves the reliability of power electronic devices by new material/ structure/ process, redundancy, and derating operation, which is becoming increasingly difficult to meet the requirements of rapidly developing power conversion. Based on the needs of reliability research, a data-driven lifetime prediction method for thermal stress fatigue failure of power MOSFETs is proposed. The main work is reflected in two aspects: (1) The thermal stress fatigue failure mechanism of the power MOSFETs is analyzed. On-state resistance is selected as the failure precursor parameter for evaluating the health status of power MOSFETs. (2) Autoregressive Integrated Moving Average (ARIMA) model of Time-Series Analysis is applied to realize data-driven lifetime prediction. Compared with the model-based lifetime prediction method using nonlinear regression algorithm, The data-driven method has higher prediction accuracy and better prediction stability.