Physics-informed learning under epistemic uncertainty with an application to system health modeling


This study proposes a methodology for developing deterioration models to estimate the remaining lifetime of a system using physics-informed learning. The approach consists of combining physical knowledge about the system with a set of data obtained from similar systems that have failed in the past to build a set of constraints on the evolution over time of the state of health of the system. The data consists of partial measurements of the system variables, taken from its commissioning to the present. The physical knowledge used comprises a set of differential equations that approximate the dynamics and aging of the system. In contrast to other studies, the physical model is not assumed to be accurate, but is considered to be an approximation of reality. Constraints on the temporal evolution of the state of health derived from this physical knowledge take into account its imprecision and consist of possibility distributions. In this study, the max-max, max-min and min-max regret principles are applied to extract the time evolution of the deterioration rate that best fits the constraints. The effectiveness of the proposed algorithm is evaluated by means of a comparative empirical analysis in different use cases, and the situations in which informed learning improves on purely data-driven algorithms are analyzed.

In International Journal of Approximate Reasoning
Nahuel Costa
Nahuel Costa
Machine Learning & AI researcher