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Aircraft Bleed Air System Fault Prediction based on Encoder-Decoder with Attention Mechanism
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Nanjing University of Aeronautics and Astronautics, China
Submission date: 2023-02-14
Final revision date: 2023-03-30
Acceptance date: 2023-06-08
Online publication date: 2023-06-12
Publication date: 2023-06-12
Corresponding author
Youchao Sun   

Nanjing University of Aeronautics and Astronautics, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(3):167792
  • A novel fault prediction method for the aircraft bleed air system is proposed by combining the DSTP-ED prediction model and the EWMA control chart.
  • The DSTP-ED model incorporates attention mechanisms and has more accurate prediction results compared to other models.
  • The EWMA control chart can effectively identify impending bleed air system failures.
  • The proposed method is validated with real airline QAR data.
The engine bleed air system (BAS) is one of the important systems for civil aircraft, and fault prediction of BAS is necessary to improve aircraft safety and the operator's profit. A dual-stage two-phase attention-based encoder-decoder (DSTP-ED) prediction model is proposed for BAS normal state estimation. Unlike traditional ED networks, the DSTP-ED combines spatial and temporal attention to better capture the spatiotemporal relationships to achieve higher prediction accuracy. Five data-driven algorithms, autoregressive integrated moving average (ARIMA), support vector regression (SVR), long short-term memory (LSTM), ED, and DSTP-ED, are applied to build prediction models for BAS. The comparison experiments show that the DSTP-ED model outperforms the other four data-driven models. An exponentially weighted moving average (EWMA) control chart is used as the evaluation criterion for the BAS failure warning. An empirical study based on Quick Access Recorder (QAR) data from Airbus A320 series aircraft demonstrates that the proposed method can effectively predict failures.
This work was supported by the Joint Fund of the National Natural Science Foundation of China and Civil Aviation Administration of China (U2033202, U1333119); and the National Natural Science Foundation of China (No. 52172387).
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