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Data-driven Prognostics based on Evolving Fuzzy Degradation Models for Power Semiconductor Devices

BOUTROUS, K.; BESSA, I. V.; PUIG, V.; NEJJARI, F.; PALHARES, R. M

2022 • 7th European Conference of the PHM Society 2022 • 7 citações

Resumo

The increasing application of power converter systems based on semiconductor devices such as Insulated-Gate Bipolar Transistors (IGBTs) has motivated the investigation of strategies for their prognostics and health management. However, physicsbased degradation modelling for semiconductors is usually complex and depends on uncertain parameters, which motivates the use of data-driven approaches. This paper addresses the problem of data-driven prognostics of IGBTs based on evolving fuzzy models learned from degradation data streams. The model depends on two classes of degradation features: one group of features that are very sensitive to the degradation stages is used as a premise variable of the fuzzy model, and another group that provides good trendability and monotonicity is used for the auto-regressive consequent of the fuzzy model for degradation prediction. This strategy allows obtaining interpretable degradation models, which are improved when more degradation data is obtained from the Unit Under Test (UUT) in real time. Furthermore, the fuzzy-based Remaining Useful Life (RUL) prediction is equipped with an uncertainty quantification mechanism to better aid decisionmakers. The proposed approach is then used for the RUL prediction considering an accelerated aging IGBT dataset from the NASA Ames Research Center.

palavras-chave

Palavras-chave

data-driven prognosticspower semiconductor devicesremaining useful life predictiondegradation modelinguncertainty quantificationaccelerated agingprognostics and health managementpower converters

Status

Published

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