2023

1. Data-Driven Remaining Useful Life Estimation of Discrete Power Electronic Devices

   Wilhelm Söderkvist Vermelin, Andreas Lövberg, Maciej Misiorny, and 2 more authors

   In Proceedings of the 33rd European Safety and Reliability Conference (ESREL 2023), 2023

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   Robust and accurate prognostics models for estimation of remaining useful life (RUL) are becoming an increasingly important aspect of research in reliability and safety in modern electronic components and systems. In this work, a data driven approach to the prognostics problem is presented. In particular, machine learning models are trained to predict the RUL of wire-bonded silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs) subjected to power cycling until failure. During such power cycling, ON-state voltage and various temperature measurements are continuously collected. As the data set contains full run-to-failure trajectories, the issue of estimating RUL is naturally formulated in terms of supervised learning. Three neural network architectures were trained, evaluated, and compared on the RUL problem: a temporal convolutional neural network (TCN), a long short-term memory neural network (LSTM) and a convolutional gated recurrent neural network (Conv-GRU). While the results show that all networks perform well on held out testing data if the testing samples are of similar aging acceleration as the samples in the training data set, performance on out-of-distribution data is significantly lower. To this end, we discuss potential research directions to improve model performance in such scenarios.

   @inproceedings{data-driven-sic-mosfet-rul-estimation,
     author = {Söderkvist Vermelin, Wilhelm and Lövberg, Andreas and Misiorny, Maciej and P. Eng, Mattias and Brinkfeldt, Klas},
     publisher = {European Safety and Reliability Conference},
     booktitle = {Proceedings of the 33rd European Safety and Reliability
                    Conference (ESREL 2023)},
     title = {Data-Driven Remaining Useful Life Estimation of Discrete Power
                Electronic Devices},
     year = {2023},
     volume = {33},
     doi = {10.3850/978-981-18-8071-1-4procd},
     url = {https://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-67109},
   }

2. A Simple Hybrid Model for Estimating Remaining Useful Life of SiC MOSFETs in Power Cycling Experiments

   Mattias P Eng, Andreas Lövberg, Maciej Misiorny, and 3 more authors

   In PHM Society Asia-Pacific Conference, 2023

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   Recording and prediction of the accumulated damage, which will eventually lead to the failure of power electronic modules, is an aspect of high importance for power electronic systems design and, in particular, for development of Prognostic and Health Management (PHM) schemes for in-field applications. To this end, this paper presents a simple and cost-effective prognostic method for predicting the remaining useful life (RUL) of TO-247 packaged silicon carbide (SiC) metal-oxide semiconductor field-effect transistors (MOSFETs) subjected to power cycling experiments. The model assumes that the major failure mode is bond-wire lift-off and uses a damage accumulation scheme based on Paris’ crack law. The only inputs to the model are historical data on the average junction temperature swing and the temperature-compensated drain-source ON-state resistance at the peak temperature of the current cycle. Using only these two input values, the model is shown to predict RUL with surprising accuracy for the range of constant current loads determining cycling conditions under which the test data series have been acquired. This work is a first step in an ongoing project towards building more elaborate prognostic schemes for RUL-determination of SiC power MOSFETs in actual working conditions, using physics-informed neural networks (PINNs).

   @inproceedings{eng2023simple,
     title = {A Simple Hybrid Model for Estimating Remaining Useful Life of SiC
                MOSFETs in Power Cycling Experiments},
     author = {Eng, Mattias P and L{\"o}vberg, Andreas and Misiorny, Maciej and Vermelin, Wilhelm S{\"o}derkvist and Brinkfeldt, Klas and Mishra, Madhav},
     booktitle = {PHM Society Asia-Pacific Conference},
     volume = {4},
     number = {1},
     year = {2023},
   }