Research Papers

Degradation Modeling and Remaining Useful Life Prediction of Aircraft Engines Using Ensemble Learning

[+] Author and Article Information
Zhixiong Li

Department of Mechanical and
Aerospace Engineering,
University of Central Florida,
Orlando, FL 32816
e-mail: zhixiong.li@Knights.ucf.edu

Kai Goebel

NASA Ames Research Center,
Moffett Field, CA 95134;
Division of Operation and
Maintenance Engineering,
Luleå University of Technology,
Luleå 971 87, Sweden
e-mail: kai.goebel@nasa.gov

Dazhong Wu

Department of Mechanical and
Aerospace Engineering,
University of Central Florida,
Orlando, FL 32816
e-mail: dazhong.wu@ucf.edu

1Corresponding author.

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.

J. Eng. Gas Turbines Power 141(4), 041008 (Nov 16, 2018) (10 pages) Paper No: GTP-18-1207; doi: 10.1115/1.4041674 History: Received May 13, 2018; Revised October 02, 2018

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.

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Fig. 1

A computational framework for the ensemble learning-based prognostics

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Fig. 2

Generating new training data points from the original training data for training unit ID-1

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Fig. 3

Simplified diagram of the aircraft engine simulated in C-MAPSS [4]

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Fig. 4

Variable importance of 21 variables

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Fig. 5

Health indices associated with 249 training units when using (a) 21 variables, (b) 3 variables [26], (c) 11 variables [27], and (d) 7 variables selected by RFs

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Fig. 6

RUL prediction performance on CV-test data with seven variables: (a) base learners and (b) ensemble learning

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Fig. 7

RUL predictions for one CV-test unit: (a) base learners and (b) ensemble learning

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Fig. 8

RUL prediction for 248 test units using (a) base learners and (b) ensemble learning



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