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A weighted fuzzy C-means clustering method with density peak for anomaly detection in IoT-enabled manufacturing process

Author

Listed:
  • Shaohua Huang

    (Tsinghua University
    Nanjing University of Aeronautics and Astronautics)

  • Yu Guo

    (Nanjing University of Aeronautics and Astronautics)

  • Nengjun Yang

    (Nanjing University of Aeronautics and Astronautics)

  • Shanshan Zha

    (Nanjing University of Aeronautics and Astronautics)

  • Daoyuan Liu

    (Nanjing University of Aeronautics and Astronautics)

  • Weiguang Fang

    (Nanjing University of Aeronautics and Astronautics)

Abstract

Accurate anomaly detection is the premise of production process control and normal execution of production plan. The implementation of Internet of Things (IoT) provides data foundation and guarantee for real-time perception and detection of production state. Taking abundant IoT data as support, a density peak (DP)-weighted fuzzy C-means (WFCM) based clustering method is proposed to detect abnormal situations in production process. Firstly, a features correlation and redundancy measure method based on mutual information (MI) and conditional MI is proposed, unsupervised feature reduction is completed based on the principle of maximum correlation-minimum redundancy. Secondly, a DP-WFCM based clustering model is established to identify clusters with fewer samples to detect production anomalies. DP is used to obtain the initial clustering centers to solve the problem that FCM is sensitive to the initial centers and the clusters number needs to be determined manually in advance. MI-based similarities are introduced as weight coefficients to guide the clustering process, which improves convergence speed and clustering quality. Finally, a real case from an IoT enabled machining workshop is carried out to verify the accuracy and effectiveness of the proposed method in anomaly detection of manufacturing process.

Suggested Citation

  • Shaohua Huang & Yu Guo & Nengjun Yang & Shanshan Zha & Daoyuan Liu & Weiguang Fang, 2021. "A weighted fuzzy C-means clustering method with density peak for anomaly detection in IoT-enabled manufacturing process," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1845-1861, October.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:7:d:10.1007_s10845-020-01690-y
    DOI: 10.1007/s10845-020-01690-y
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    References listed on IDEAS

    as
    1. Juan Pablo Usuga Cadavid & Samir Lamouri & Bernard Grabot & Robert Pellerin & Arnaud Fortin, 2020. "Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1531-1558, August.
    2. Xiaohua Cao & Tiffany Li & Qiang Wang, 2019. "RFID-based multi-attribute logistics information processing and anomaly mining in production logistics," International Journal of Production Research, Taylor & Francis Journals, vol. 57(17), pages 5453-5466, September.
    3. Semchedine Fedala & Didier Rémond & Rabah Zegadi & Ahmed Felkaoui, 2018. "Contribution of angular measurements to intelligent gear faults diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1115-1131, June.
    4. Jiewu Leng & Pingyu Jiang, 2019. "Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 979-994, March.
    5. Chuang Wang & Pingyu Jiang, 2019. "Deep neural networks based order completion time prediction by using real-time job shop RFID data," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1303-1318, March.
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    Cited by:

    1. Ma, Shuaiyin & Huang, Yuming & Liu, Yang & Liu, Haizhou & Chen, Yanping & Wang, Jin & Xu, Jun, 2023. "Big data-driven correlation analysis based on clustering for energy-intensive manufacturing industries," Applied Energy, Elsevier, vol. 349(C).

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