Product quality prediction model based on generative adversarial network and hard case mining

被引:0
|
作者
Li, Jianfeng [1 ]
Bai, Xue [1 ]
Zhao, Chuncai [2 ]
Qian, Pengchao [2 ]
Wang, Hongtao [1 ]
Xu, Weifeng [3 ]
机构
[1] School of Economics and Management, China Jiliang University, Hangzhou,310018, China
[2] Department of Quality Management, Xinfengming Group Research Institute, Tongxiang,314513, China
[3] Research and Development Center, Hangzhou GUPO Technology, Hangzhou,311200, China
来源
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2024年 / 30卷 / 10期
基金
中国国家自然科学基金;
关键词
Adversarial machine learning;
D O I
10.13196/j.cims.2023.0532
中图分类号
学科分类号
摘要
According to the characteristics of process industries, the issue of low recall in identifying defective prod-ucts caused by imbalanced dass was addressed. To extract effective features from high-dimensional data, the advan-tages of one dass F-score and mRMR in feature extraction were combined to effectively reduce the feature dimension and extract valuable features. Then, the Wasserstein Generative Adversarial Network (WGAN) algorithm was em-ployed to augment the quantity of defective product. Subsequently, the focal loss function was optimized with dass weights to enhance the recognition rate of hard case. Furthermore, leveraging the LightGBM algorithm in conjunction with a threshold movement strategy, a quality prediction model was constructed based on WGAN and hard case mining techniques. Finally, the proposed model was applied to the open-source SECOM dataset, and the result indicated that the presented approach effectively enhanced the recall rate of defective products while maintai-ning Overall accuracy, which provided a scientific and practical method for in-depth exploration of the intricate map-ping relationship between critical production factors and product quality, as well as facilitating intelligent quality prediction efforts. © 2024 CIMS. All rights reserved.
引用
收藏
页码:3698 / 3707
相关论文
共 50 条
  • [31] Wind Power Prediction considering Ramping Events Based on Generative Adversarial Network
    Huang Q.
    Huang, Qiyue (839539199@qq.com), 1600, Hindawi Limited (2021):
  • [32] TranGAN: Generative Adversarial Network based Transfer Learning for Social Tie Prediction
    Chen, Yanjiao
    Xiong, Yuxuan
    Liu, Bulou
    Yin, Xiaoyan
    ICC 2019 - 2019 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2019,
  • [33] Sea Surface Temperature Prediction Method Based on Deep Generative Adversarial Network
    Wang, Jia
    Zheng, Gang
    Yu, Jiali
    Shao, Jinliang
    Zhou, Yinfei
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 17 : 14704 - 14711
  • [34] Medicine Expenditure Prediction via a Variance- Based Generative Adversarial Network
    Kaushik, Shruti
    Choudhury, Abhinav
    Natarajan, Sayee
    Pickett, Larry A., Jr.
    Dutt, Varun
    IEEE ACCESS, 2020, 8 : 110947 - 110958
  • [35] Stock Price Prediction Method Based on Sentiment Analysis and Generative Adversarial Network
    Liu Y.
    Zhao G.
    Zou Z.
    Wu S.
    Hunan Daxue Xuebao/Journal of Hunan University Natural Sciences, 2022, 49 (10): : 111 - 118
  • [36] Image Region Prediction from Thermal Videos Based on Image Prediction Generative Adversarial Network
    Batchuluun, Ganbayar
    Koo, Ja Hyung
    Kim, Yu Hwan
    Park, Kang Ryoung
    MATHEMATICS, 2021, 9 (09)
  • [37] A Water Quality Prediction Model Based on Knowledge-enhanced Deep Adversarial Network
    Yan, Jianzhuo
    Gao, Qingcai
    Chen, Jianhui
    PROCEEDINGS OF 2021 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY WORKSHOPS AND SPECIAL SESSIONS: (WI-IAT WORKSHOP/SPECIAL SESSION 2021), 2021, : 284 - 289
  • [38] Unsupervised Domain Adaptation Classification Model Based on Generative Adversarial Network
    Wang G.-G.
    Guo T.
    Yu Y.
    Su H.
    Guo, Tao (tguo@sicnu.edu.cn), 1600, Chinese Institute of Electronics (48): : 1190 - 1197
  • [39] Structural Nonlinear Model Updating Based on an Improved Generative Adversarial Network
    Yuan, Zi-Qing
    Xin, Yu
    Wang, Zuo-Cai
    Ding, Ya-Jie
    Wang, Jun
    Wang, Dong-Hui
    STRUCTURAL CONTROL & HEALTH MONITORING, 2023, 2023
  • [40] Traffic identification model based on generative adversarial deep convolutional network
    Dong, Shi
    Xia, Yuanjun
    Peng, Tao
    ANNALS OF TELECOMMUNICATIONS, 2022, 77 (9-10) : 573 - 587