標題: Concept Drift Detection and Adaption in Big Imbalance Industrial IoT Data Using an Ensemble Learning Method of Offline Classifiers
作者: Lin, Chun-Cheng
Deng, Der-Jiunn
Kuo, Chin-Hung
Chen, Linnan
工業工程與管理學系
Department of Industrial Engineering and Management
關鍵字: Ensemble learning;imbalance data;concept drift;data adaption;smart manufacturing;Industry 4.0
公開日期: 1-Jan-2019
摘要: In a smart factory, thousands of industrial Internet of Things (IIoT) devices or sensors are installed in production machines to collect big data on machine conditions and transmit it to a cyber-physical system in the cloud center of the factory. Then, the system employs a variety of condition-based maintenance (CBM) methods to predict the time point when machines start to be operated abnormally and to maintain them or replace their components in advance so as to avoid manufacturing enormous detective products. CBM suffers from problems of concept drifts (i.e., the distribution of fault patterns may change over time) and imbalance data (i.e., the data with faults accounts for a minority of all data). Ensemble learning that integrates the diversity of multiple classifiers provides a high-performance solution to address these problems. In practice, most companies may not have a sufficient budget to establish a sound infrastructure to support real-time online classifiers, but may have off-the-shelf offline classifiers in their existing systems. However, most previous works on ensemble learning only focused on supporting online classifiers. Consequently, this work proposes an ensemble learning algorithm that supports offline classifiers to cope with three-stage CBM with concept drifts and imbalance data, in which Stages 1 (training an ensemble classifier) and 3 (creating a new ensemble) employ an improved Dynamic AdaBoost.NC classifier and the SMO method to address imbalance data; and Stage 2 (detecting concept drifts in imbalance data) employs an improved LFR (Linear Four Rates) method. The experimental results on datasets with different degrees of imbalance show that the proposed method can successfully detect all concept drifts, and has a high accuracy rate in detecting minority-class data, which is over 94%.
URI: http://dx.doi.org/10.1109/ACCESS.2019.2912631
http://hdl.handle.net/11536/151999
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2912631
期刊: IEEE ACCESS
Volume: 7
起始頁: 56198
結束頁: 56207
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