Degradation modeling applied to residual lifetime prediction using functional data analysis

TL;DR

This paper introduces a nonparametric Bayesian framework for modeling degradation signals from sensors, enabling real-time residual lifetime prediction of engineering components with sparse or short-interval data.

Contribution

It presents a novel nonparametric degradation model combined with an empirical Bayes approach for online updating, applicable to real-world and simulated degradation data.

Findings

Effective residual lifetime estimation demonstrated on crack growth data

Real-time updating improves prediction accuracy

Framework handles sparse and short-interval degradation signals

Abstract

Sensor-based degradation signals measure the accumulation of damage of an engineering system using sensor technology. Degradation signals can be used to estimate, for example, the distribution of the remaining life of partially degraded systems and/or their components. In this paper we present a nonparametric degradation modeling framework for making inference on the evolution of degradation signals that are observed sparsely or over short intervals of times. Furthermore, an empirical Bayes approach is used to update the stochastic parameters of the degradation model in real-time using training degradation signals for online monitoring of components operating in the field. The primary application of this Bayesian framework is updating the residual lifetime up to a degradation threshold of partially degraded components. We validate our degradation modeling approach using a real-world crack growth data set as well as a case study of simulated degradation signals.