An Introduction to 2023 PHM Data Challenge: The Elephant in the Room and an Analysis of Competition Results

Abstract

The trend in diagnostics and prognostics for PHM is shifting toward explainable data-driven models. However, complex engineered systems are typically challenging to develop entirely explainable models for, whether they are grounded in physics or data-driven techniques. Consequently, the development of machine learning models, including hybrid variants capable of both interpolation and extrapolation, holds significant promise for enhancing the practicality of system simulation, analysis, modeling, and control in industry. The primary objective of this data challenge is to encourage contributions that expand the scope of model generalization beyond the training domain. The second aim of this data challenge is to quantify model uncertainty and methods to incorporate it into predictions. For most PHM tasks, clear guidance of the required action is ideal. To issue a definitive guidance to end users, it is useful to quantify uncertainty for the whole model. This data challenge addresses both estimation and uncertainty.