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Analyzing the operation reliability of aeroengine using Quick Access Recorder flight data

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  • Pan, Wei-Huang
  • Feng, Yun-Wen
  • Lu, Cheng
  • Liu, Jia-Qi

Abstract

Aeroengine operation reliability (AOR) estimation is important for stakeholders in operating, monitoring, designing, and improving. The Quick Access Recorder (QAR) flight data and failure rate of aeroengine are utilized to analyze AOR. Considering the uncertainty in AOR assessment, a Bayesian neural network (BNN) is trained to evaluate and forecast AOR based on aeroengine status data within a confidence interval. Further, to quantify the degree of each feature on AOR, Shapley Additive ex-Planations (SHAP) values are calculated based on the Light gradient boosting machine (LightGBM) to study the degree and direction of influence feature on AOR. In this study, it is revealed that (i) AOR is closely related to the airplane flight stages; and (ii) after training with eight flights and validation with two flights data from QAR data, BNN can achieve AOR analysis and prediction within a certain confidence interval while obtaining aeroengine state data; and (iii) the feature importance and influence direction are quantified by SHAP values, it demonstrates the sensitive factors in AOR analysis. Based QAR data, this study provide an AOR analysis framework to improve the operation and design, which has the potential to support aeroengine real-time status monitoring and health management.

Suggested Citation

  • Pan, Wei-Huang & Feng, Yun-Wen & Lu, Cheng & Liu, Jia-Qi, 2023. "Analyzing the operation reliability of aeroengine using Quick Access Recorder flight data," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    • Handle: RePEc:eee:reensy:v:235:y:2023:i:c:s0951832023001084
    DOI: 10.1016/j.ress.2023.109193
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    References listed on IDEAS

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    1. Lu, Cheng & Teng, Da & Chen, Jun-Yu & Fei, Cheng-Wei & Keshtegar, Behrooz, 2023. "Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    2. Wang, Jian & Gao, Shibin & Yu, Long & Zhang, Dongkai & Ding, Chugang & Chen, Ke & Kou, Lei, 2022. "Predicting wind-caused floater intrusion risk for overhead contact lines based on Bayesian neural network with spatiotemporal correlation analysis," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    3. Zhu, Shun-Peng & Huang, Hong-Zhong & Peng, Weiwen & Wang, Hai-Kun & Mahadevan, Sankaran, 2016. "Probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 146(C), pages 1-12.
    4. Sarazin, Gabriel & Morio, Jérôme & Lagnoux, Agnès & Balesdent, Mathieu & Brevault, Loïc, 2021. "Reliability-oriented sensitivity analysis in presence of data-driven epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. Wang, Run-Zi & Gu, Hang-Hang & Zhu, Shun-Peng & Li, Kai-Shang & Wang, Ji & Wang, Xiao-Wei & Hideo, Miura & Zhang, Xian-Cheng & Tu, Shan-Tung, 2022. "A data-driven roadmap for creep-fatigue reliability assessment and its implementation in low-pressure turbine disk at elevated temperatures," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    6. Li, Xiaoke & Zhu, Heng & Chen, Zhenzhong & Ming, Wuyi & Cao, Yang & He, Wenbin & Ma, Jun, 2022. "Limit state Kriging modeling for reliability-based design optimization through classification uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    7. Jia, Xiang & Cheng, Zhijun & Guo, Bo, 2022. "Reliability analysis for system by transmitting, pooling and integrating multi-source data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    8. Bhardwaj, U. & Teixeira, A.P. & Guedes Soares, C., 2022. "Bayesian framework for reliability prediction of subsea processing systems accounting for influencing factors uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    9. Li, Xiao-Yang & Chen, Wen-Bin & Kang, Rui, 2021. "Performance margin-based reliability analysis for aircraft lock mechanism considering multi-source uncertainties and wear," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
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