Calibration, validation, and selection of hydrostatic testing-based remaining useful life prediction models for polyethylene pipes

A novel methodology for model selection among competing models for remaining useful life (RUL) prediction is developed in this paper. Due to the long durability of polyethylene (PE) pipes under normal conditions, life data under normal operating conditions is not available for model validation and selection. Physics-based, regression-based, and hybrid models for RUL prediction are available in the literature based on experimental data under accelerated test conditions. These models need to be evaluated for prediction performance under normal conditions, but in the absence of data under normal conditions. A model consistency-based metric for selecting the best model in the absence of data under normal conditions is proposed in this paper. The consistency-based metric evaluates the predictive consistency of the various probabilistic RUL prediction models over the estimated or known probability distribution of the normal operating conditions. A two-step approach for model selection is proposed: first, validation evaluation using empirical data, and second, consistency evaluation under likely operating conditions. The proposed model selection methodology is demonstrated using five candidate models for RUL prediction of PE pipes based on accelerated hydrostatic testing data. Bayesian inference is used to calibrate these models with empirical data, and its benefit over the least squares approach recommended in ISO 9080 is demonstrated. Further, the proposed two-step model selection methodology is compared against traditional model selection methods based on goodness of fit, model complexity and information theoretic metrics. It is seen that the proposed additional consistency criterion is successful in selecting the best model compared to existing methods that are unable to distinguish between the different available models.