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The use of condition monitoring (CM) signals to predict the remaining useful life of in-service units plays a critical role in reliability engineering. Many models assume that CM signals behave under similar external conditions or that external factors have no effect on the evolution of these signals. These assumptions might not hold in real-life applications. In this paper, we propose a nonparametric framework for modeling the evolution of CM signals under different external factors. The unique feature of our model is that it does not assume any functional form for CM signals and is able to incorporate the effect of external factors through a reparametrization technique called hypersphere decomposition. Through extensive numerical studies and a case study on automotive lead-acid batteries, we demonstrate the advantageous features of our proposed method specifically when the evolution of CM signals is impacted by external factors. [ABSTRACT FROM PUBLISHER]

Remaining useful life prediction plays an important role in ensuring the safety, availability, and efficiency of various engineering systems. In this paper, we propose a flexible Bayesian multiple-phase modeling approach to characterize degradation signals for prognosis. The priors are specified with a novel stochastic process and the multiple-phase model is formulated to a novel state-space model to facilitate online monitoring and prediction. A particle filtering algorithm with stratified sampling and partial Gibbs resample-move strategy is developed for online model updating and residual life prediction. The advantages of the proposed method are demonstrated through extensive numerical studies and real case studies. [ABSTRACT FROM PUBLISHER]