Stochastic RUL calculation enhanced with TDNN-based IGBT failure modeling

Show simple item record Alghassi, Alireza Perinpanayagam, Suresh Samie, Mohammad 2016-06-30T10:07:04Z 2016-06-30T10:07:04Z 2016-05-30
dc.identifier.citation Alireza Alghassi, Suresh Perinpanayagam and Mohammad Samie. Stochastic RUL calculation enhanced with TDNN-based IGBT failure modeling. IEEE Transactions on Reliability, Volume:65, Issue:2, pp558-573 en_UK
dc.identifier.issn 0018-9529
dc.description.abstract Power electronics are widely used in the transport and energy sectors. Hence, the reliability of these power electronic components is critical to reducing the maintenance cost of these assets. It is vital that the health of these components is monitored for increasing the safety and availability of a system. The aim of this paper is to develop a prognostic technique for estimating the remaining useful life (RUL) of power electronic components. There is a need for an efficient prognostic algorithm that is embeddable and able to support on-board real-time decision-making. A time delay neural network (TDNN) is used in the development of failure modes for an insulated gate bipolar transistor (IGBT). Initially, the time delay neural network is constructed from training IGBTs' ageing samples. A stochastic process is performed for the estimation results to compute the probability of the health state during the degradation process. The proposed TDNN fusion with a statistical approach benefits the probability distribution function by improving the accuracy of the results of the TDDN in RUL prediction. The RUL (i.e., mean and confidence bounds) is then calculated from the simulation of the estimated degradation states. The prognostic results are evaluated using root mean square error (RMSE) and relative accuracy (RA) prognostic evaluation metrics. en_UK
dc.language.iso en en_UK
dc.publisher Institute of Electrical and Electronics Engineers en_UK
dc.rights Attribution-NonCommercial 4.0 International
dc.title Stochastic RUL calculation enhanced with TDNN-based IGBT failure modeling en_UK
dc.type Article en_UK
dc.identifier.cris 5499166

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