Remaining useful life prediction: A multiple product partition approach. Issue 9 (27th September 2022)

• Record Type: Journal Article
• Authors: Lau, John W.; Cripps, Edward; Cripps, Sally
• Is Part Of: Communications in statistics. Volume 51:Issue 9(2022)
• Page Start: 5288
• Page End: 5307
• Publication Date: 2022-09-27
• Subjects: Remaining useful life -- Product partition model -- Degradation -- Vibration measurements; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2020.1766499
• Languages: English
• ISSNs: 0361-0918

Abstract

Abstract: 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.