Journal of East China Normal University(Natural Science) ›› 2022, Vol. 2022 ›› Issue (5): 219-232.doi: 10.3969/j.issn.1000-5641.2022.05.018

• Spatio-temporal Data Analysis and Intelligent Optimization Theory for Logistics • Previous Articles    

Prediction of remaining useful life of aeroengines based on the Transformer with multi-feature fusion

Yilin MA, Huiling TAO, Qiwen DONG, Ye WANG*()   

  1. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
  • Received:2022-07-10 Online:2022-09-25 Published:2022-09-26
  • Contact: Ye WANG


As the core components of aircraft, engines play a vital role during flight. Accurate prediction of the remaining useful life of the aeroengine can help prognostics and health management, thus preventing major accidents and saving maintenance costs. In view of the lack of consideration of different time steps and the relationship between different sensors and operating conditions in existing methods, a remaining useful life prediction method based on the Transformer was proposed, which fuses multi-feature outputs from different encoder layers. This method selects two input data with different time steps, analyzes the relationship between the sensors using permutation entropy, and extracts features independently from the operating condition data. The experimental results on the public aeroengine dataset CMAPSS (Commercial Modular Aero-Propulsion System Simulation) show that the proposed method is superior to other advanced remaining useful life prediction methods.

Key words: deep learning, remaining useful life, aeroengine, Transformer

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