IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i14p8575-d861901.html
   My bibliography  Save this article

Machine-Learning-Based System for the Detection of Entanglement in Dyeing and Finishing Processes

Author

Listed:
  • Chien-Chih Wang

    (Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 24301, Taiwan)

  • Yu-Hsun Li

    (Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 24301, Taiwan)

Abstract

Many dyeing and finishing factories generally use old-fashioned dyeing machines. A key issue when using these machines is that the dyeing tank cannot detect entanglement problems, which may result in a lower dyeing quality. In this paper, imbalanced data with ensemble machine learning, such as Extreme Gradient Boosting (XGBoost) and random forest (RF), are integrated to predict the possible states of a dyeing machine, including normal operation, entanglement warning, and entanglement occurrence. To verify the results obtained using the proposed method, we worked with industry−academia collaborators. We collected 1,750,977 pieces of data from 1848 batches. The results obtained from the analysis show that after employing the Borderline synthetic minority oversampling technique and the Tomek link to deal with the data imbalance, combined with the model established by XGBoost, the prediction accuracy of the normal operation states, entanglement warning, and entanglement occurrence were 100%, 94%, and 96%, respectively. Finally, the proposed entanglement detection system was connected with the factory’s central control system using a web application programming interface and machine real-time operational parameter data. Thus, a real-time tangle anomaly warning and monitoring system was developed for the actual operating conditions.

Suggested Citation

  • Chien-Chih Wang & Yu-Hsun Li, 2022. "Machine-Learning-Based System for the Detection of Entanglement in Dyeing and Finishing Processes," Sustainability, MDPI, vol. 14(14), pages 1-12, July.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8575-:d:861901
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/14/8575/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/14/8575/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Xiao-Han & Zhu, Qun-Xiong & He, Yan-Lin & Xu, Yuan, 2018. "A novel robust ensemble model integrated extreme learning machine with multi-activation functions for energy modeling and analysis: Application to petrochemical industry," Energy, Elsevier, vol. 162(C), pages 593-602.
    2. Antoniadis, Anestis & Lambert-Lacroix, Sophie & Poggi, Jean-Michel, 2021. "Random forests for global sensitivity analysis: A selective review," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jung, WoongHee & Taflanidis, Alexandros A., 2023. "Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. Pilowsky, Julia A. & Manica, Andrea & Brown, Stuart & Rahbek, Carsten & Fordham, Damien A., 2022. "Simulations of human migration into North America are more sensitive to demography than choice of palaeoclimate model," Ecological Modelling, Elsevier, vol. 473(C).
    3. Lei, Hongxuan & Liu, Pan & Cheng, Qian & Xu, Huan & Liu, Weibo & Zheng, Yalian & Chen, Xiangding & Zhou, Yong, 2024. "Frequency, duration, severity of energy drought and its propagation in hydro-wind-photovoltaic complementary systems," Renewable Energy, Elsevier, vol. 230(C).
    4. Ma, Yuan-Zhuo & Jin, Xiang-Xiang & Zhao, Xiang & Li, Hong-Shuang & Zhao, Zhen-Zhou & Xu, Chang, 2024. "Reliability-oriented global sensitivity analysis using subset simulation and space partition," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    5. Hongquan Gui & Jialan Liu & Chi Ma & Mengyuan Li, 2024. "Industrial-oriented machine learning big data framework for temporal-spatial error prediction and control with DTSMGCN model," Journal of Intelligent Manufacturing, Springer, vol. 35(3), pages 1173-1196, March.
    6. Georgios Spanos & Antonios Lalas & Konstantinos Votis & Dimitrios Tzovaras, 2025. "Principal Component Random Forest for Passenger Demand Forecasting in Cooperative, Connected, and Automated Mobility," Sustainability, MDPI, vol. 17(6), pages 1-13, March.
    7. Gao, Zhikun & Yu, Junqi & Zhao, Anjun & Hu, Qun & Yang, Siyuan, 2022. "A hybrid method of cooling load forecasting for large commercial building based on extreme learning machine," Energy, Elsevier, vol. 238(PC).
    8. Dela Rosa & Berna Elya & Muhammad Hanafi & Alfi Khatib & Eka Budiarto & Syamsu Nur & Muhammad Imam Surya, 2025. "Investigation of alpha-glucosidase inhibition activity of Artabotrys sumatranus leaf extract using metabolomics, machine learning and molecular docking analysis," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-32, January.
    9. Li, Wei & Liu, Xing & Lu, Can, 2023. "Analysis of China's steel response ways to EU CBAM policy based on embodied carbon intensity prediction," Energy, Elsevier, vol. 282(C).
    10. Wang, Zheng-Xin & He, Ling-Yang & Zheng, Hong-Hao, 2019. "Forecasting the residential solar energy consumption of the United States," Energy, Elsevier, vol. 178(C), pages 610-623.
    11. Djandja, Oraléou Sangué & Salami, Adekunlé Akim & Wang, Zhi-Cong & Duo, Jia & Yin, Lin-Xin & Duan, Pei-Gao, 2022. "Random forest-based modeling for insights on phosphorus content in hydrochar produced from hydrothermal carbonization of sewage sludge," Energy, Elsevier, vol. 245(C).
    12. Manuel Quintero & William T. Stephenson & Advik Shreekumar & Tamara Broderick, 2025. "Common Functional Decompositions Can Mis-attribute Differences in Outcomes Between Populations," Papers 2504.16864, arXiv.org.
    13. Xiong, Qingwen & Du, Peng & Deng, Jian & Huang, Daishun & Song, Gongle & Qian, Libo & Wu, Zenghui & Luo, Yuejian, 2022. "Global sensitivity analysis for nuclear reactor LBLOCA with time-dependent outputs," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    14. Kévin Elie-Dit-Cosaque & Véronique Maume-Deschamps, 2024. "Random forest based quantile-oriented sensitivity analysis indices estimation," Computational Statistics, Springer, vol. 39(4), pages 1747-1777, June.
    15. Simsekler, Mecit Can Emre & Rodrigues, Clarence & Qazi, Abroon & Ellahham, Samer & Ozonoff, Al, 2021. "A comparative study of patient and staff safety evaluation using tree-based machine learning algorithms," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    16. Zhang, Xiaodong & Dimitrov, Nikolay, 2024. "Variable importance analysis of wind turbine extreme responses with Shapley value explanation," Renewable Energy, Elsevier, vol. 232(C).
    17. Ling Tao & Yuanlai Xie & Chundong Hu, 2022. "Efficient Sensitivity Analysis for Enhanced Heat Transfer Performance of Heat Sink with Swirl Flow Structure under One-Side Heating," Energies, MDPI, vol. 15(19), pages 1-19, October.
    18. Kim, Jun Young & Kim, Dongjae & Li, Zezhong John & Dariva, Claudio & Cao, Yankai & Ellis, Naoko, 2023. "Predicting and optimizing syngas production from fluidized bed biomass gasifiers: A machine learning approach," Energy, Elsevier, vol. 263(PC).
    19. Mehdi Dasineh & Amir Ghaderi & Mohammad Bagherzadeh & Mohammad Ahmadi & Alban Kuriqi, 2021. "Prediction of Hydraulic Jumps on a Triangular Bed Roughness Using Numerical Modeling and Soft Computing Methods," Mathematics, MDPI, vol. 9(23), pages 1-24, December.
    20. Zheng Jiang & Shuohua Zhang & Wei Li, 2022. "Exploration of Urban Emission Mitigation Pathway under the Carbon Neutrality Target: A Case Study of Beijing, China," Sustainability, MDPI, vol. 14(21), pages 1-18, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8575-:d:861901. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.