A weighted fuzzy C-means clustering method with density peak for anomaly detection in IoT-enabled manufacturing process
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DOI: 10.1007/s10845-020-01690-y
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References listed on IDEAS
- 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.
- 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.
- 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.
- 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.
- 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.
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Cited by:
- 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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Keywords
Anomaly detection; Internet of Things; Weighted fuzzy C-means; Clustering; Feature reduction;All these keywords.
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