Taylor DQN: An Optimization Method for Aircraft Engine Cleaning Schedule

Rui Wang, Xiangyu Guo (), Zhiqi Yan and Dongqi Chen
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Rui Wang: School of Ocean Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China
Xiangyu Guo: Weihai Key Laboratory of Intelligent Operation and Maintenance, Harbin Institute of Technology, Weihai 264209, China
Zhiqi Yan: Aeronautical Engineering Institute, Civil Aviation University of China, Tianjin 061102, China
Dongqi Chen: Juxian Agricultural Machinery Development Service Center, Rizhao 222113, China

Mathematics, 2023, vol. 11, issue 19, 1-22

Abstract: Reducing carbon emissions and improving revenue in the face of global warming and economic challenges is a growing concern for airlines. This paper addresses the inefficiencies and high costs associated with current aero-engine on-wing washing strategies. To tackle this issue, we propose a reinforcement learning framework consisting of a Similar Sequence Method and a Taylor DQN model. The Similar Sequence Method, comprising a sample library, DTW algorithm, and boundary adjustment, predicts washed aero-engine data for the Taylor DQN model. Leveraging the proposed Taylor neural networks, our model outputs Q-values to make informed washing decisions using data from the Similar Sequence Method. Through simulations, we demonstrate the effectiveness of our approach.

Keywords: aircraft engine cleaning schedule; reinforcement learning; Taylor DQN model; similar sequence method (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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