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DyS-IENN: a novel multiclass imbalanced learning method for early warning of tardiness in rocket final assembly process

Author

Listed:
  • Zilong Zhuang

    (Shanghai Jiao Tong University)

  • Liangxun Guo

    (Shanghai Jiao Tong University)

  • Zizhao Huang

    (Shanghai Jiao Tong University)

  • Yanning Sun

    (Shanghai Jiao Tong University)

  • Wei Qin

    (Shanghai Jiao Tong University)

  • Zhao-Hui Sun

    (Shanghai Jiao Tong University)

Abstract

Establishing an effective early warning mechanism for the rocket final assembly process (RFAP) is crucial for the timely delivery of rockets and the reduction of additional production costs. To solve the unsystematic design of warning indicators and warning levels in RFAP and address the problem of low warning accuracy caused by imbalanced data distribution, this paper redesigns the warning indicators and warning levels in a systematic way, and develops a novel multiclass imbalanced learning method based on dynamic sampling algorithm (DyS) and improved ensemble neural network (IENN). The DyS algorithm dynamically determines the training set after oversampling the minority class, while the IENN can effectively suppress the oscillation in the iterative process of the DyS algorithm and improve the overall classification accuracy by removing the redundant and ineffective networks from the ensemble neural network. The experiment results indicate that the proposed method outperforms other methods in terms of accuracy and stability for early warning of tardiness in RFAP.

Suggested Citation

  • Zilong Zhuang & Liangxun Guo & Zizhao Huang & Yanning Sun & Wei Qin & Zhao-Hui Sun, 2021. "DyS-IENN: a novel multiclass imbalanced learning method for early warning of tardiness in rocket final assembly process," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2197-2207, December.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:8:d:10.1007_s10845-020-01631-9
    DOI: 10.1007/s10845-020-01631-9
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    References listed on IDEAS

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    1. Qiang Zhou & Ping Yan & Huayi Liu & Yang Xin, 2019. "A hybrid fault diagnosis method for mechanical components based on ontology and signal analysis," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1693-1715, April.
    2. Xu Tan & Lining Xing & Zhaoquan Cai & Gaige Wang, 2020. "Analysis of production cycle-time distribution with a big-data approach," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1889-1897, December.
    3. Feyza Gürbüz & İkbal Eski & Berrin Denizhan & Cihan Dağlı, 2019. "Prediction of damage parameters of a 3PL company via data mining and neural networks," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1437-1449, March.
    4. Junliang Wang & Jie Zhang, 2016. "Big data analytics for forecasting cycle time in semiconductor wafer fabrication system," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7231-7244, December.
    5. Amit Kumar Jain & Bhupesh Kumar Lad, 2019. "A novel integrated tool condition monitoring system," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1423-1436, March.
    6. Arash Ramezani & Hendrik Rothe, 2017. "Simulation-Based Early Prediction of Rocket, Artillery, and Mortar Trajectories and Real-Time Optimization for Counter-RAM Systems," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-8, August.
    7. Carlos Gonzalez-Val & Adrian Pallas & Veronica Panadeiro & Alvaro Rodriguez, 2020. "A convolutional approach to quality monitoring for laser manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 789-795, March.
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