Accurate Remaining Useful Life Prediction with Uncertainty Quantification: a Deep Learning and Nonstationary Gaussian Process Approach

TL;DR

This paper introduces a novel deep learning combined with nonstationary Gaussian process regression model for highly accurate RUL prediction with uncertainty quantification, outperforming existing models on NASA turbofan engine data.

Contribution

It presents a new integrated model that effectively handles high-dimensional data, noise robustness, and time-dependency in RUL prediction, with superior accuracy and uncertainty bounds.

Findings

Root mean square error 1.7 to 6.2 times smaller than competitors

RUL uncertainty bounds are valid and tighter

Model outperforms other data-driven RUL prediction models

Abstract

Remaining useful life (RUL) refers to the expected remaining lifespan of a component or system. Accurate RUL prediction is critical for prognostic and health management and for maintenance planning. In this work, we address three prevalent challenges in data-driven RUL prediction, namely the handling of high dimensional input features, the robustness to noise in sensor data and prognostic datasets, and the capturing of the time-dependency between system degradation and RUL prediction. We devise a highly accurate RUL prediction model with uncertainty quantification, which integrates and leverages the advantages of deep learning and nonstationary Gaussian process regression (DL-NSGPR). We examine and benchmark our model against other advanced data-driven RUL prediction models using the turbofan engine dataset from the NASA prognostic repository. Our computational experiments show that the DL-NSGPR predictions are highly accurate with root mean square error 1.7 to 6.2 times smaller than those of competing RUL models. Furthermore, the results demonstrate that RUL uncertainty bounds with the proposed DL-NSGPR are both valid and significantly tighter than other stochastic RUL prediction models. We unpack and discuss the reasons for this excellent performance of the DL-NSGPR.