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Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics

Author

Listed:
  • Manuel Arias Chao

    (Chair of Intelligent Maintenance Systems, ETH Zürich, 8093 Zürich, Switzerland
    Current address: HIL H 27.3, Stefano-Franscini-Platz 5, 8093 Zürich, Switzerland.)

  • Chetan Kulkarni

    (KBR, Inc., NASA Ames Research Center, Mountain View, CA 94035, USA)

  • Kai Goebel

    (Operation and Maintenance, Luleå University of Technology, 971 87 Luleå, Sweden
    PARC, Intelligent Systems Lab, Palo Alto, CA 94043, USA)

  • Olga Fink

    (Chair of Intelligent Maintenance Systems, ETH Zürich, 8093 Zürich, Switzerland)

Abstract

A key enabler of intelligent maintenance systems is the ability to predict the remaining useful lifetime (RUL) of its components, i.e., prognostics. The development of data-driven prognostics models requires datasets with run-to-failure trajectories. However, large representative run-to-failure datasets are often unavailable in real applications because failures are rare in many safety-critical systems. To foster the development of prognostics methods, we develop a new realistic dataset of run-to-failure trajectories for a fleet of aircraft engines under real flight conditions. The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) model developed at NASA. The damage propagation modelling used in this dataset builds on the modelling strategy from previous work and incorporates two new levels of fidelity. First, it considers real flight conditions as recorded on board of a commercial jet. Second, it extends the degradation modelling by relating the degradation process to its operation history. This dataset also provides the health, respectively, fault class. Therefore, besides its applicability to prognostics problems, the dataset can be used for fault diagnostics.

Suggested Citation

  • Manuel Arias Chao & Chetan Kulkarni & Kai Goebel & Olga Fink, 2021. "Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics," Data, MDPI, vol. 6(1), pages 1-14, January.
    • Handle: RePEc:gam:jdataj:v:6:y:2021:i:1:p:5-:d:479890
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    References listed on IDEAS

    as
    1. Shi, Zunya & Chehade, Abdallah, 2021. "A dual-LSTM framework combining change point detection and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    2. Li, Xiang & Ding, Qian & Sun, Jian-Qiao, 2018. "Remaining useful life estimation in prognostics using deep convolution neural networks," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 1-11.
    3. Hu, Chao & Youn, Byeng D. & Wang, Pingfeng & Taek Yoon, Joung, 2012. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 120-135.
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    Cited by:

    1. Bajarunas, Kristupas & Baptista, Marcia L. & Goebel, Kai & Chao, Manuel Arias, 2024. "Health index estimation through integration of general knowledge with unsupervised learning," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    2. Wang, Yilin & Li, Yuanxiang & Zhang, Yuxuan & Lei, Jia & Yu, Yifei & Zhang, Tongtong & Yang, Yongshen & Zhao, Honghua, 2024. "Incorporating prior knowledge into self-supervised representation learning for long PHM signal," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    3. Zhou, Liang & Wang, Huawei & Xu, Shanshan, 2023. "Aero-engine prognosis strategy based on multi-scale feature fusion and multi-task parallel learning," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    4. Chen, Jiaxian & Li, Dongpeng & Huang, Ruyi & Chen, Zhuyun & Li, Weihua, 2023. "Aero-engine remaining useful life prediction method with self-adaptive multimodal data fusion and cluster-ensemble transfer regression," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. Wang, Yilin & Shen, Lei & Zhang, Yuxuan & Li, Yuanxiang & Zhang, Ruixin & Yang, Yongshen, 2023. "Self-supervised Health Representation Decomposition based on contrast learning," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    6. Nejjar, Ismail & Geissmann, Fabian & Zhao, Mengjie & Taal, Cees & Fink, Olga, 2024. "Domain adaptation via alignment of operation profile for Remaining Useful Lifetime prediction," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    7. Liao, Zengbu & Zhan, Keyi & Zhao, Hang & Deng, Yuntao & Geng, Jia & Chen, Xuefeng & Song, Zhiping, 2024. "Addressing class-imbalanced learning in real-time aero-engine gas-path fault diagnosis via feature filtering and mapping," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    8. Salman Khalid & Jinwoo Song & Muhammad Muzammil Azad & Muhammad Umar Elahi & Jaehun Lee & Soo-Ho Jo & Heung Soo Kim, 2023. "A Comprehensive Review of Emerging Trends in Aircraft Structural Prognostics and Health Management," Mathematics, MDPI, vol. 11(18), pages 1-42, September.

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