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, USA

Kai Goebel

NASA Ames Research Center, Moffett Field, CA 95134, USA; Luleå University of Technology, Division of Operation and Maintenance Engineering, Luleå, Sweden

Dazhong Wu

Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL 32816, USA

1Corresponding author.

ASME doi:10.1115/1.4041674 History: Received May 13, 2018; Revised October 02, 2018


Degradation modeling and prediction of remaining useful life (RUL) is crucial in 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 data-driven predictive modeling methods. The objective of this study is to introduce an ensemble learning-based prognostics approach to damage propagation modeling and prognostics of failures in aircraft engines. This 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 make accurate predictions, 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 reported prognostic methods.

Section 4: U.S. Gov Employees + Reg Authors
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