Asymmetric ϵ-Support Vectors Regression for Remaining Useful Life Distribution Estimation

Estimating the remaining useful life (RUL) of industrial components is essential in predictive maintenance practice. In real-world applications, both aleatoric uncertainties (e.g., randomness due to uncontrollable factors) and epistemic uncertainties (e.g., ignorance about the model correctness) exist. Therefore, uncertainty quantification is expected to be associated with the point estimation, i.e., an estimation of the RUL probability density function (PDF) is necessary. However, existing data-driven methods which can give a probabilistic estimation of RUL have limitations when applying to real-world industrial systems: statistical methods often rely on certain assumptions on the degradation process; probabilistic machine learning methods usually have large complexity and need some prior knowledge on the parameter distribution and/or likelihood model.