Uncertainty Quantification in Industrial Systems Using Deep Gaussian Process for Accurate Degradation Modeling

Abstract

Several factors, such as human error, environmental factors, and maintenance practices, contribute to the degradation of real-world industrial systems. Predicting system dynamics is challenging and necessitates high user confidence, as these factors contribute to both aleatoric uncertainty (inherent system variability) and epistemic uncertainty (due to limited information). Decision-making and risk assessment are frequently hindered by the inability of current artificial intelligence methods to generate interpretable uncertainty estimates. To address these constraints, we propose an analysis that employs Deep Gaussian Processes (DGPs), a robust framework for generating interpretable uncertainty distributions and capturing system variability. A rigorous mathematical foundation is essential to our approach, because it enables the selection of metrics that effectively capture the system’s degradation aspects. In addition to predicting the remaining useful life, these metrics, when used in conjunction with DGPs, facilitate the creation of a degradation model that is both accurate and dependable. This model also contributes to the improvement of system reliability and proactive maintenance. We demonstrate our approach’s practical efficacy by validating it on a real-world industrial semolina plant with four mills.