Remaining Useful Life Estimation Under Uncertainty with Causal GraphNets

TL;DR

This paper introduces a GraphNets-based method for estimating remaining useful life in non-stationary, large-scale time series with uncertainty quantification, demonstrating superior performance on simulated and real-world datasets.

Contribution

It presents a novel GraphNets approach for RUL estimation that handles non-equispaced, multi-scale features and incorporates uncertainty modeling, advancing beyond traditional recurrent models.

Findings

Effective on simulated stochastic degradation data

Successful application to real-world ball-bearings dataset

Provides uncertainty estimates as Gamma distributions

Abstract

In this work, a novel approach for the construction and training of time series models is presented that deals with the problem of learning on large time series with non-equispaced observations, which at the same time may possess features of interest that span multiple scales. The proposed method is appropriate for constructing predictive models for non-stationary stochastic time series.The efficacy of the method is demonstrated on a simulated stochastic degradation dataset and on a real-world accelerated life testing dataset for ball-bearings. The proposed method, which is based on GraphNets, implicitly learns a model that describes the evolution of the system at the level of a state-vector rather than of a raw observation. The proposed approach is compared to a recurrent network with a temporal convolutional feature extractor head (RNN-tCNN) which forms a known viable alternative for the problem context considered. Finally, by taking advantage of recent advances in the computation of reparametrization gradients for learning probability distributions, a simple yet effective technique for representing prediction uncertainty as a Gamma distribution over remaining useful life predictions is employed.