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

Shaohua Huang (), Yu Guo, Nengjun Yang, Shanshan Zha, Daoyuan Liu and Weiguang Fang
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Shaohua Huang: Tsinghua University
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

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 7, No 5, 1845-1861

Abstract: 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.

Keywords: Anomaly detection; Internet of Things; Weighted fuzzy C-means; Clustering; Feature reduction (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10845-020-01690-y

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