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June 2011 Degradation modeling applied to residual lifetime prediction using functional data analysis
Rensheng R. Zhou, Nicoleta Serban, Nagi Gebraeel
Ann. Appl. Stat. 5(2B): 1586-1610 (June 2011). DOI: 10.1214/10-AOAS448

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.

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Rensheng R. Zhou. Nicoleta Serban. Nagi Gebraeel. "Degradation modeling applied to residual lifetime prediction using functional data analysis." Ann. Appl. Stat. 5 (2B) 1586 - 1610, June 2011. https://doi.org/10.1214/10-AOAS448

Information

Published: June 2011
First available in Project Euclid: 13 July 2011

zbMATH: 1223.62156
MathSciNet: MR2849787
Digital Object Identifier: 10.1214/10-AOAS448

Keywords: Condition Monitoring , functional principal component analysis , nonparametric estimation , residual life distribution , sparse degradation signal

Rights: Copyright © 2011 Institute of Mathematical Statistics

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Vol.5 • No. 2B • June 2011
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