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http://dx.doi.org/10.36498/kbigdt.2021.6.1.23

A Study on the Design of Supervised and Unsupervised Learning Models for Fault and Anomaly Detection in Manufacturing Facilities  

Oh, Min-Ji (충북대학교 대학원 빅데이터학과)
Choi, Eun-Seon (충북대학교 대학원 빅데이터학과)
Roh, Kyung-Woo (충북대학교 경영정보학과)
Kim, Jae-Sung (충북대학교 대학원 빅데이터학과)
Cho, Wan-Sup (충북대학교 경영정보학과)
Publication Information
The Journal of Bigdata / v.6, no.1, 2021 , pp. 23-35 More about this Journal
Abstract
In the era of the 4th industrial revolution, smart factories have received great attention, where production and manufacturing technology and ICT converge. With the development of IoT technology and big data, automation of production systems has become possible. In the advanced manufacturing industry, production systems are subject to unscheduled performance degradation and downtime, and there is a demand to reduce safety risks by detecting and reparing potential errors as soon as possible. This study designs a model based on supervised and unsupervised learning for detecting anomalies. The accuracy of XGBoost, LightGBM, and CNN models was compared as a supervised learning analysis method. Through the evaluation index based on the confusion matrix, it was confirmed that LightGBM is most predictive (97%). In addition, as an unsupervised learning analysis method, MD, AE, and LSTM-AE models were constructed. Comparing three unsupervised learning analysis methods, the LSTM-AE model detected 75% of anomalies and showed the best performance. This study aims to contribute to the advancement of the smart factory by combining supervised and unsupervised learning techniques to accurately diagnose equipment failures and predict when abnormal situations occur, thereby laying the foundation for preemptive responses to abnormal situations. do.
Keywords
Sensor data; Fault and Anomaly Detection; XGBoost; LightGBM; MD; AE; LSTM-AE; CNN;
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