Source: Ann. Appl. Stat.
Volume 5, Number 2B
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
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