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New fusion and selection approaches for estimating the remaining useful life using Gaussian process regression and induced ordered weighted averaging operators
Author(s) -
Bouzenita Mohammed,
Mouss LeilaHayet,
Melgani Farid,
Bentrcia Toufik
Publication year - 2020
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2688
Subject(s) - fusion , context (archaeology) , hyperparameter , computer science , covariance , selection (genetic algorithm) , gaussian process , gaussian , regression , process (computing) , artificial intelligence , kriging , data mining , sensor fusion , machine learning , pattern recognition (psychology) , algorithm , mathematics , statistics , paleontology , philosophy , linguistics , physics , quantum mechanics , biology , operating system
In this paper, we propose new fusion and selection approaches to accurately predict the remaining useful life. The fusion scheme is built upon the combination of outcomes delivered by an ensemble of Gaussian process regression models. Each regressor is characterized by its own covariance function and initial hyperparameters. In this context, we adopt the induced ordered weighted averaging as a fusion tool to achieve such combination. Two additional fusion techniques based on the simple averaging and the ordered weighted averaging operators besides a selection approach are implemented. The differences between adjacent elements of the raw data are used for training instead of the original values. Experimental results conducted on lithium‐ion battery data report a significant improvement in the obtained results. This work may provide some insights regarding the development of efficient intelligent fusion alternatives for further prognostic advances.