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contributor authorLi, Zhixiong
contributor authorGoebel, Kai
contributor authorWu, Dazhong
date accessioned2019-03-17T10:54:08Z
date available2019-03-17T10:54:08Z
date copyright11/16/2018 12:00:00 AM
date issued2019
identifier issn0742-4795
identifier othergtp_141_04_041008.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4256382
description abstractDegradation modeling and prediction of remaining useful life (RUL) are crucial to prognostics and health management of aircraft engines. While model-based methods have been introduced to predict the RUL of aircraft engines, little research has been reported on estimating the RUL of aircraft engines using novel data-driven predictive modeling methods. The objective of this study is to introduce an ensemble learning-based prognostic approach to modeling an exponential degradation process due to wear as well as predicting the RUL of aircraft engines. The ensemble learning algorithm combines multiple base learners, including random forests (RFs), classification and regression tree (CART), recurrent neural networks (RNN), autoregressive (AR) model, adaptive network-based fuzzy inference system (ANFIS), relevance vector machine (RVM), and elastic net (EN), to achieve better predictive performance. The particle swarm optimization (PSO) and sequential quadratic optimization (SQP) methods are used to determine optimum weights that are assigned to the base learners. The predictive model trained by the ensemble learning algorithm is demonstrated on the data generated by the commercial modular aero-propulsion system simulation (C-MAPSS) tool. Experimental results have shown that the ensemble learning algorithm predicts the RUL of the aircraft engines with considerable robustness as well as outperforms other prognostic methods reported in the literature.
publisherThe American Society of Mechanical Engineers (ASME)
titleDegradation Modeling and Remaining Useful Life Prediction of Aircraft Engines Using Ensemble Learning
typeJournal Paper
journal volume141
journal issue4
journal titleJournal of Engineering for Gas Turbines and Power
identifier doi10.1115/1.4041674
journal fristpage41008
journal lastpage041008-10
treeJournal of Engineering for Gas Turbines and Power:;2019:;volume( 141 ):;issue: 004
contenttypeFulltext


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