This paper presents a computationally efficient prognostic approach for predicting the remaining useful life of power electronic components, specifically using isolated-gate bipolar transistors (IGBTs) as a case study. The proposed algorithms are designed to be embeddable for real-time decision making, addressing the limitations of current Markov model-based methods which are too intensive for real-time applications. By analyzing failure mechanisms and degradation data from accelerated aging experiments, the study explores various probability distributions for improved modeling of IGBT degradation.

1. COMPUTATIONALLY EFFICIENT, REAL-TIME,
AND EMBEDDABLE PROGNOSTIC TECHNIQUES
FOR POWER ELECTRONICS
ABSTRACT:
Power electronics are increasingly important in new generation vehicles as
critical safety mechanical subsystems are being replaced with more electronic components.
Hence, it is vital that the health of these power electronic components is monitored for safety and
reliability on a platform. The aim of this paper is to develop a prognostic approach for predicting
the remaining useful life of power electronic components. The developed algorithms must also
be embeddable and computationally efficient to support on-board real-time decision making.
Current state-of-the-art prognostic algorithms, notably those based on Markov models, are
computationally intensive and not applicable to real-time embedded applications. In this paper,
an isolated-gate bipolar transistor (IGBT) is used as a case study for prognostic development.
The proposed approach is developed by analyzing failure mechanisms and statistics of IGBT
degradation data obtained from an accelerated aging experiment. The approach explores various
probability distributions for modeling discrete degradation profiles of the IGBT component. This
allows the stochastic degradation model to be efficiently simulated, in this particular example
∼1000 times more efficiently than Markov approaches.