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

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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.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China under Grant No. 51775348 and No. U1637211.

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Authors and Affiliations

  1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Zilong Zhuang, Liangxun Guo, Zizhao Huang, Yanning Sun, Wei Qin & Zhao-Hui Sun

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  1. Zilong Zhuang
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  2. Liangxun Guo
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  3. Zizhao Huang
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  4. Yanning Sun
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  5. Wei Qin
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  6. Zhao-Hui Sun
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Correspondence to Wei Qin.

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Zhuang, Z., Guo, L., Huang, Z. et al. DyS-IENN: a novel multiclass imbalanced learning method for early warning of tardiness in rocket final assembly process. J Intell Manuf 32, 2197–2207 (2021). https://doi.org/10.1007/s10845-020-01631-9

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  • DOI: https://doi.org/10.1007/s10845-020-01631-9

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