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A Data-Driven Lifetime Prediction Method for Thermally Aged SiC MOSFET Applications | IEEE Conference Publication | IEEE Xplore

A Data-Driven Lifetime Prediction Method for Thermally Aged SiC MOSFET Applications


Abstract:

Power semiconductor switches, such as Metal-Oxide Semiconductor Field-Effect Transistor (MOSFETs), are widely utilized in solid-state power controllers (SSPC) of electric...Show More

Abstract:

Power semiconductor switches, such as Metal-Oxide Semiconductor Field-Effect Transistor (MOSFETs), are widely utilized in solid-state power controllers (SSPC) of electric vehicles, aircrafts and trains. Predictive Health Monitoring (PHM), coupled with the reliability analysis of MOSFETs, are of ut-most significance in power electronic systems. Among various PHM indicators, the ON-state resistance of MOSFETs stands out as a vital and indicative harbinger of failure. This paper introduces a data-driven methodology employing a Long-Short Term Memory (LSTM) algorithm to predict the variations of the ON-state resistance. The experimental dataset was derived from subjecting the MOSFET to power cycling under thermal stress conditions. Furthermore, the model's efficacy was scrutinized utilizing a minor fraction of the dataset for training the LSTM algorithm, showcasing robust performance. Additionally, the proposed model was validated across diverse MOSFET degradation datasets, affirming its universal applicability.
Date of Conference: 28-31 May 2024
Date Added to IEEE Xplore: 17 September 2024
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Conference Location: Stockholm, Sweden

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