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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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References

Lee, S. , Ma, Y.-S. , Thimm, G. , and Verstraeten, J. , 2008, “ Product Lifecycle Management in Aviation Maintenance, Repair and Overhaul,” Comput. Ind., 59(2–3), pp. 296–303. [CrossRef]
Wu, D. , Jennings, C. , Terpenny, J. , Gao, R. X. , and Kumara, S. , 2017, “ A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests,” ASME J. Manuf. Sci. Eng., 139(7), p. 071018. [CrossRef]
Li, Z. , Wu, D. , Hu, C. , and Terpenny, J. , 2017, “ An Ensemble Learning-Based Prognostic Approach With Degradation-Dependent Weights for Remaining Useful Life Prediction,” Reliab. Eng. Syst. Saf. (in press).
Saxena, A. , Goebel, K. , Simon, D. , and Eklund, N. , 2008, “ Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation,” International Conference on Prognostics and Health Management (PHM), Denver, CO, Oct. 6–9, pp. 1–9.
Schwabacher, M. , and Goebel, K. , 2018, “ A Survey of Artificial Intelligence for Prognostics,” AAAI Fall Symposium, Arlington, VA, Oct. 18–20, pp. 107–114.
Wang, P. , and Gao, R. X. , 2016, “ Markov Nonlinear System Estimation for Engine Performance Tracking,” ASME J. Eng. Gas Turbines Power, 138(9), p. 091201. [CrossRef]
Mosallam, A. , Medjaher, K. , and Zerhouni, N. , 2016, “ Data-Driven Prognostic Method Based on Bayesian Approaches for Direct Remaining Useful Life Prediction,” J. Intell. Manuf., 27(5), pp. 1037–1048. [CrossRef]
Liu, K. , and Huang, S. , 2016, “ Integration of Data Fusion Methodology and Degradation Modeling Process to Improve Prognostics,” IEEE Trans. Autom. Sci. Eng., 13(1), pp. 344–354. [CrossRef]
Nieto, P. G. , Garcia-Gonzalo, E. , Lasheras, F. S. , and de Cos Juez, F. J. , 2015, “ Hybrid PSO–SVM-Based Method for Forecasting of the Remaining Useful Life for Aircraft Engines and Evaluation of Its Reliability,” Reliab. Eng. Syst. Saf., 138, pp. 219–231. [CrossRef]
Khelif, R. , Chebel-Morello, B. , Malinowski, S. , Laajili, E. , Fnaiech, F. , and Zerhouni, N. , 2017, “ Direct Remaining Useful Life Estimation Based on Support Vector Regression,” IEEE Trans. Ind. Electron., 64(3), pp. 2276–2285. [CrossRef]
Yu, J. , 2017, “ Aircraft Engine Health Prognostics Based on Logistic Regression With Penalization Regularization and State-Space-Based Degradation Framework,” Aerosp. Sci. Technol., 68, pp. 345–361. [CrossRef]
Hu, C. , Youn, B. D. , Wang, P. , and Yoon, J. T. , 2012, “ Ensemble of Data-Driven Prognostic Algorithms for Robust Prediction of Remaining Useful Life,” Reliab. Eng. Syst. Saf., 103, pp. 120–135. [CrossRef]
Ramasso, E. , and Gouriveau, R. , 2014, “ Remaining Useful Life Estimation by Classification of Predictions Based on a Neuro-Fuzzy System and Theory of Belief Functions,” IEEE Trans. Reliab., 63(2), pp. 555–566. [CrossRef]
Chen, H. , 2011, “ A Multiple Model Prediction Algorithm for CNC Machine Wear PHM,” Int. J. Prognostics Health Manage., 2, p. 129. https://www.phmsociety.org/sites/phmsociety.org/files/phm_submission/2010/ijPHM_11_011.pdf
Goebel, K. , Eklund, N. , and Bonanni, P. , 2006, “ Fusing Competing Prediction Algorithms for Prognostics,” IEEE Aerospace Conference, Big Sky, MT, Mar. 4–11, p. 10.
Kennedy, J. , 2011, “ Particle Swarm Optimization,” Encyclopedia of Machine Learning, Springer, Boston, MA, pp. 760–766.
Breiman, L. , 2001, “ Random Forests,” Mach. Learn., 45(1), pp. 5–32. [CrossRef]
Prasad, A. M. , Iverson, L. R. , and Liaw, A. , 2006, “ Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction,” Ecosystems, 9(2), pp. 181–199. https://doi.org/10.1007/s10021-005-0054-1
Loh, W. Y. , 2011, “ Classification and Regression Trees,” Wiley Interdiscip. Rev.: Data Min. Knowl. Discovery, 1(1), pp. 14–23. [CrossRef]
Lee, J. , Wu, F. , Zhao, W. , Ghaffari, M. , Liao, L. , and Siegel, D. , 2014, “ Prognostics and Health Management Design for Rotary Machinery Systems—Reviews, Methodology and Applications,” Mech. Syst. Signal Process., 42(1–2), pp. 314–334. [CrossRef]
Čerňanský, M. , Makula, M. , and Beňušková, Ľ. , 2007, “ Organization of the State Space of a Simple Recurrent Network before and After Training on Recursive Linguistic Structures,” Neural Networks, 20(2), pp. 236–244. [CrossRef] [PubMed]
Akaike, H. , 1969, “ Fitting Autoregressive Models for Prediction,” Ann. Institute Stat. Math., 21(1), pp. 243–247. [CrossRef]
Jang, J.-S. , 1993, “ ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Trans. Syst., Man, Cybern., 23(3), pp. 665–685. [CrossRef]
Tipping, M. E. , 2001, “ Sparse Bayesian Learning and the Relevance Vector Machine,” J. Mach. Learn. Res., 1, pp. 211–244.
Zou, H. , and Hastie, T. , 2005, “ Regularization and Variable Selection Via the Elastic Net,” J. R. Stat. Soc.: Ser. B (Stat. Methodology), 67(2), pp. 301–320. [CrossRef]
Fang, X. , Paynabar, K. , and Gebraeel, N. , 2017, “ Multistream Sensor Fusion-Based Prognostics Model for Systems With Single Failure Modes,” Reliab. Eng. Syst. Saf., 159, pp. 322–331. [CrossRef]
Yan, H. , Liu, K. , Zhang, X. , and Shi, J. , 2016, “ Multiple Sensor Data Fusion for Degradation Modeling and Prognostics Under Multiple Operational Conditions,” IEEE Trans. Reliab., 65(3), pp. 1416–1426. [CrossRef]
Nocedal, J. , and Wright, S. J. , 2006, Sequential Quadratic Programming, Springer, New York.
Babu, G. S. , Zhao, P. , and Li, X.-L. , “ Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life,” Database Systems for Advanced Applications (Lecture Notes in Computer Science, Vol. 9642), S. Navathe, W. Wu, S. Shekhar, X. Du, X. Wang, and H. Xiong, eds., Springer, Cham, Switzerland, pp. 214–228.
Zhang, C. , Lim, P. , Qin, A. , and Tan, K. C. , 2017, “ Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics,” IEEE Trans. Neural Networks Learn. Syst., 28(10), pp. 2306–2318. [CrossRef]
Malinowski, S. , Chebel-Morello, B. , and Zerhouni, N. , 2015, “ Remaining Useful Life Estimation Based on Discriminating Shapelet Extraction,” Reliab. Eng. Syst. Saf., 142, pp. 279–288. [CrossRef]
Li, X. , Ding, Q. , and Sun, J.-Q. , 2018, “ Remaining Useful Life Estimation in Prognostics Using Deep Convolution Neural Networks,” Reliab. Eng. Syst. Saf., 172, pp. 1–11. [CrossRef]

Figures

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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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