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Production Quality Evaluation of Electronic Control Modules Based on Deep Belief Network

Author

Listed:
  • Hua Gong

    (School of Science, Shenyang Ligong University, Shenyang 110159, China
    Liaoning Key Laboratory of Intelligent Optimization and Control for Ordnance Industry, Shenyang 110159, China
    These authors contributed equally to this work.)

  • Wanning Xu

    (Liaoning Key Laboratory of Intelligent Optimization and Control for Ordnance Industry, Shenyang 110159, China)

  • Congang Chen

    (Faculty of Science and Technology, Beijing Normal University—Hong Kong Baptist University United International College, Zhuhai 519087, China)

  • Wenjuan Sun

    (School of Science, Shenyang Ligong University, Shenyang 110159, China
    Liaoning Key Laboratory of Intelligent Optimization and Control for Ordnance Industry, Shenyang 110159, China
    These authors contributed equally to this work.)

Abstract

The electronic control module is an important part of a digital electronic detonator, which undergoes a complex production process that includes three electrical performance tests and three visual inspection procedures. In each inspection procedure, several different types of data are generated daily, including numerical and categorical data. To evaluate the production quality of electronic control modules, an algorithm based on a Deep Belief Network with Multi-mutation Differential Evolution (MDE-DBN) is designed in this study. First, key indicators are extracted to construct a production quality evaluation index system. A Multi-mutation Differential Evolution algorithm is designed to optimize the initial network weights of the Deep Belief Network (DBN) and integrate the production quality information into the pre-training phase. Subsequently, the preprocessed experimental data are input into the MDE-DBN algorithm to obtain the distributions of excellent, general, and unqualified production statuses, verifying the effectiveness of the algorithm. The experimental results show that the MDE-DBN algorithm has significant advantages in evaluation accuracy when compared with DBNs improved by other intelligent optimization algorithms and machine learning methods.

Suggested Citation

  • Hua Gong & Wanning Xu & Congang Chen & Wenjuan Sun, 2024. "Production Quality Evaluation of Electronic Control Modules Based on Deep Belief Network," Mathematics, MDPI, vol. 12(23), pages 1-26, November.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:23:p:3799-:d:1534068
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    References listed on IDEAS

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    1. Ding, Kun & Chen, Xiang & Weng, Shuai & Liu, Yongjie & Zhang, Jingwei & Li, Yuanliang & Yang, Zenan, 2023. "Health status evaluation of photovoltaic array based on deep belief network and Hausdorff distance," Energy, Elsevier, vol. 262(PB).
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