A weighted fuzzy C-means clustering method with density peak for anomaly detection in IoT-enabled manufacturing process
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A weighted fuzzy C-means clustering method with density peak for anomaly detection in IoT-enabled manufacturing process Shaohua Huang1,2
· Yu Guo2 · Nengjun Yang2 · Shanshan Zha2 · Daoyuan Liu2 · Weiguang Fang2
Received: 30 May 2020 / Accepted: 4 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
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
Introduction Facing with fierce market competition and diverse customer needs, modern manufacturing industry has shifted from make-to-stock to make-to-order (MTO) production (Wang and Jiang 2019). Diversified products require various operational resources, which makes the production process more and more complicated, dynamically changing and error-prone (Cao et al. 2019). Material arrival delay, workin-process (WIP) machining timeout, buffer accumulation and other factors will directly affect the normal operation of manufacturing system. These anomalies seriously lower the efficiency and accuracy of production operation. However, current MTO manufacturing enterprises have high demands for the punctuality of order delivery, which requires timely
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Shaohua Huang [email protected]
1
Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
2
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
detection of abnormal factors affecting production progress and rapid positioning of the root causes of abnormal production, so as to ensure the timely regulation of abnormal conditions and the normal execution of production plans. Accurate anomaly detection is of great significance for st
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