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

Zilong Zhuang, Liangxun Guo, Zizhao Huang, Yanning Sun, Wei Qin () and Zhao-Hui Sun
Additional contact information
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

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 8, No 8, 2197-2207

Abstract: 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.

Keywords: Early warning of tardiness; Rocket final assembly; Imbalanced learning; Ensemble neural network; Dynamic sampling (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-020-01631-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:32:y:2021:i:8:d:10.1007_s10845-020-01631-9

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-020-01631-9

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().