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Sensor-based degradation signals measure the accumulation of damage of anengineering system using sensor technology. Degradation signals can be used toestimate, for example, the distribution of the remaining life of partiallydegraded systems and/or their components. In this paper we present anonparametric degradation modeling framework for making inference on theevolution of degradation signals that are observed sparsely or over shortintervals of times. Furthermore, an empirical Bayes approach is used to updatethe stochastic parameters of the degradation model in real-time using trainingdegradation signals for online monitoring of components operating in the field.The primary application of this Bayesian framework is updating the residuallifetime up to a degradation threshold of partially degraded components. Wevalidate our degradation modeling approach using a real-world crack growth dataset as well as a case study of simulated degradation signals.

Degradation modeling applied to residual lifetime prediction using functional data analysis