Stochastic methodology for prognostics under continuously varying environmental profiles
Authors:
- 29 June 2012
- journal article
- research article
- Published by Wiley in Statistical Analysis and Data Mining: An ASA Data Science Journal
- Vol. 6 (3) , 260-270
Abstract
We present a stochastic modeling framework for sensor‐based degradation signals that predicts, in real time, the residual lifetime of individual components subjected to a time‐varying environment. We investigate the future environmental profile that is deterministic and evolves continuously. Unique to our model is the union of historical data with real‐time sensor‐based data to update the degradation model and the residual life distribution (RLD) of the component within a Bayesian framework. The performance of our model is evaluated on the basis of degradation signals from both numerical experiments and a case study using real bearing data. The results show that our approach accurately estimates the RLD by incorporating the environmental effects and utilizing the real‐time observations. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 6: 260–270, 2013Keywords
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