A hybrid system-level prognostics approach with online RUL forecasting for electronics-rich systems with unknown degradation behaviors

This paper proposes a system-level prognostic approach for power electronic systems with slow degradation profiles. Although a model-based approach has been adopted to deal with such multivariable dynamical systems with degradation properties, the forecasting of the Remaining Useful Life (RUL) is independent of prior knowledge of degradation profiles. Thus, this proposition is mainly based on the estimation of the degraded parameters. A robust and well-known technique, the Adaptive Joint Extended Kalman Filter (AJEKF), has been used in previous publications for degradation estimation. Consequently, a deep comprehension of the fault mechanisms of the critical electronic components such as Electrolytic Capacitors (ECaps) and power switching devices such as MOSFETs is needed to define their fault precursors and their degradation behaviors for analytical modeling. The developed forecasting methodology highlights the importance of the historical degradation data in the modeling and estimation stages. The main goal is to increase the reliability of the Prognostics and Health Management (PHM). Thus, this technique has been fully applied to a DC-DC converter used in electric vehicles to forecast its RUL on system-level from component-level basis and the simulation results are then presented.