author = "Shuai Zhao and Shaowei Chen and Fei Yang and
Enes Ugur and Bilal Akin and Huai Wang",
title = "A Composite Failure Precursor for Condition Monitoring
and Remaining Useful Life Prediction of Discrete Power
Devices",
journal = "IEEE Transactions on Industrial Informatics",
year = "2020",
abstract = "In order to prevent catastrophic failures in power
electronic systems, multiple failure precursors have
been identified to characterize the degradation of
power devices. However, there are some practical
challenges in determining the suitable failure
precursor which supports the high-accuracy prediction
of remaining useful life (RUL). This paper proposes a
method to formulate a composite failure precursor (CFP)
by taking full advantage of potential failure
precursors, where CFP is directly optimized in terms of
the degradation model to improve the prediction
performance. The RUL estimations of the degradation
model are explicitly derived to facilitate the
precursor quality calculation. For CFP formulation, a
genetic programming method is applied to integrate the
potential failure precursors in a nonlinear way. As a
result, a framework that can formulate a superior
failure precursor for the given RUL prediction model is
elaborated. The proposed method is validated with the
power cycling testing results of SiC MOSFETs.",