Uncertainty-Aware Prognosis via Deep Gaussian Process

Abstract

The problem of Uncertainty Quantification (UQ) is of paramount importance when Machine Learning (ML) and Deep Learning (DL) models are deployed in the real world. In the context of safety-critical applications, such as the prediction of the Remaining Useful-Life (RUL) of infrastructure and industrial assets, the relevance of effective UQ approaches is even higher, given the potentially catastrophic consequences or substantial costs associated with maintenance decisions that are performed either too late or too early. Particularly in such safety critical application contexts, transparency and reliability are essential requirements and any ML-based solution not providing meaningful uncertainty estimates would not fully satisfy such desiderata. However, most ML and DL techniques used for RUL estimation are often not designed to perform UQ, thus limiting their effective applicability in real-world scenarios. To address this limitation, in this paper we investigate the performance of a recently proposed class of algorithms, Deep Gaussian Process (DGPs), for predicting the remaining useful lifetime (RUL). DGPs provide uncertainty estimates associated with their predictions, yet retaining the expressive power and learning capabilities of modern DL methods. DGPs are able to scale to very large datasets, which was a limitation of some of the previous algorithms but has increasingly become an essential requirement for many industrial applications and, in particular, for RUL prediction tasks for which UQ is of key importance. The main contribution of this paper is a thorough evaluation and comparison of several variants of DGPs applied to the problem of RUL prediction. The performance of the DGP algorithms is evaluated on the N-CMAPSS (New Commercial Modular Aero-Propulsion System Simulation) dataset from NASA for aircraft engines. The results demonstrate that DGPs are able to provide very accurate RUL predictions along with sensible uncertainty estimates, providing more reliable solutions for (safety-critical) real-life industrial applications.