Degradation 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.
Degradation Modeling and Remaining Useful Life Prediction of Aircraft Engines Using Ensemble Learning
Moffett Field, CA 95134;
Luleå University of Technology,
Luleå 971 87, Sweden
Manuscript received May 13, 2018; final manuscript received October 2, 2018; published online November 16, 2018. Assoc. Editor: Liang Tang. This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Government's contributions.
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Li, Z., Goebel, K., and Wu, D. (November 16, 2018). "Degradation Modeling and Remaining Useful Life Prediction of Aircraft Engines Using Ensemble Learning." ASME. J. Eng. Gas Turbines Power. April 2019; 141(4): 041008. https://doi.org/10.1115/1.4041674
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