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This article introduces a Bayesian multiple change point model for a collection of degradation signals in order to predict remaining useful life of rotational bearings. The model is designed for longitudinal data, where each trajectory is a time series segmented into multiple states of degradation using a product partition structure. An efficient Markov chain Monte Carlo algorithm is designed to implement the model. The model is run on  in situ  data, where vibration measurements are taken to indicate bearing degradation. The results suggest that bearing degradation exhibit an auto-correlation structure that we incorporate into the product partition model and often experience more than one degradation phase.

Remaining useful life prediction: A multiple product partition approach