Performance assessment and RUL prediction of power converters under the multiple components degradation

DC-DC power converters are ubiquitously employed to produce an efficiently regulated voltage to a load that may be either constant or varying, from a source that may or may not be well controlled. DC-DC converters are power conversion circuits that use high-frequency switches and inductors, transformers, and capacitors to filter switching noise into regulated DC voltages. It is necessary to estimate the remaining useful life (RUL) of a power converter during operation to ensure the reliable and safe operation in aerospace, automotive, space and other mission critical applications and to provide early warning of failure for taking a pro-active action(s). This paper considers the effect of multiple components degradation on performance parameters of power converter. This study proposes a RUL prediction model by utilizing a multivariate-LSTM model to relate deviations in several performance parameters to the RUL. The superbuck power converter is used as a case study. This study follows the k-fold cross technique to validate the proposed RUL prediction model. The findings and comparison show that the multivariate-LSTM model is a better RUL predictive model with high prediction accuracy than other similar deep learning models.