Please use this identifier to cite or link to this item: https://doi.org/10.1109/TII.2018.2869429
Title: Joint Online RUL Prediction for Multivariate Deteriorating Systems
Authors: PENG WEIWEN 
YE ZHISHENG 
CHEN NAN 
Keywords: Degradation modeling
Online inference
Particle filter
Prognostics and health management (PHM)
Remaining useful life (RUL)
Issue Date: 11-Sep-2018
Publisher: IEEE Computer Society
Citation: PENG WEIWEN, YE ZHISHENG, CHEN NAN (2018-09-11). Joint Online RUL Prediction for Multivariate Deteriorating Systems. IEEE Transactions on Industrial Informatics 15 (5) : 2870-2878. ScholarBank@NUS Repository. https://doi.org/10.1109/TII.2018.2869429
Abstract: Stochastic processes and filtering methods are popular tools for degradation modeling and online remaining useful life (RUL) prediction. However, most models are for one-dimensional degradation and various filtering methods can only handle observations from a single system. This paper studies joint online RUL prediction of multi deteriorating systems with multi system observations and measurement errors. A multivariate degradation model equipped with a batch particle filter is developed and built for characterizing multiple dependent performance deteriorations with measurement errors in each system. The batch particle filter is developed for simultaneous online parameter estimation and degradation state identification by leveraging multi system observations. A numerical example and a case study are provided to demonstrate the proposed method. The results show that homogeneous multi system observations from a population of multi deteriorating systems can be jointly processed on-the-fly. Individualized online RUL prediction with improved precision for each system can be achieved through the joint online inference.
Source Title: IEEE Transactions on Industrial Informatics
URI: https://scholarbank.nus.edu.sg/handle/10635/168383
ISSN: 15513203
DOI: 10.1109/TII.2018.2869429
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