The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 5, Number 2B (2011), 1586-1610.
Degradation modeling applied to residual lifetime prediction using functional data analysis
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.
Ann. Appl. Stat., Volume 5, Number 2B (2011), 1586-1610.
First available in Project Euclid: 13 July 2011
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Zhou, Rensheng R.; Serban, Nicoleta; Gebraeel, Nagi. Degradation modeling applied to residual lifetime prediction using functional data analysis. Ann. Appl. Stat. 5 (2011), no. 2B, 1586--1610. doi:10.1214/10-AOAS448. https://projecteuclid.org/euclid.aoas/1310562734
- Supplementary material: Additional results. In this supplemental file we provide some additional results of the crack growth data study and the simulation study.