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Remaining Useful Life Estimation of Aircraft Engines Using Differentiable Architecture Search

Author

Listed:
  • Pengli Mao

    (School of Energy and Power Engineering, Beihang University, Beijing 100191, China)

  • Yan Lin

    (College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China)

  • Song Xue

    (School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China)

  • Baochang Zhang

    (Institute of Artificial Intelligence, Beihang University, Beijing 100191, China)

Abstract

Prognostics and health management (PHM) applications can prevent engines from potential serious accidents by predicting the remaining useful life (RUL). Recently, data-driven methods have been widely used to solve RUL problems. The network architecture has a crucial impact on the experiential performance. However, most of the network architectures are designed manually based on human experience with a large cost of time. To address these challenges, we propose a neural architecture search (NAS) method based on gradient descent. In this study, we construct the search space with a directed acyclic graph (DAG), where a subgraph represents a network architecture. By using softmax relaxation, the search space becomes continuous and differentiable, then the gradient descent can be used for optimization. Moreover, a partial channel connection method is introduced to accelerate the searching efficiency. The experiment is conducted on C-MAPSS dataset. In the data processing step, a fault detection method is proposed based on the k-means algorithm, which drops large valueless data and promotes the estimation performance. The experimental result shows that our method achieves superior performance with the highest estimation accuracy compared with other popular studies.

Suggested Citation

  • Pengli Mao & Yan Lin & Song Xue & Baochang Zhang, 2022. "Remaining Useful Life Estimation of Aircraft Engines Using Differentiable Architecture Search," Mathematics, MDPI, vol. 10(3), pages 1-19, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:352-:d:732195
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    References listed on IDEAS

    as
    1. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    2. Mohammad Khishe & Fabio Caraffini & Stefan Kuhn, 2021. "Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images," Mathematics, MDPI, vol. 9(9), pages 1-18, April.
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