Tehnički vjesnik, Vol. 27 No. 6, 2020.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20200906191853
Deep Learning-Guided Production Quality Estimation for Virtual Environment-Based Applications
Akm Ashiquzzaman
; Department of ICT Convergence System Engineering, Chonnam National University, Gwangju, South Korea
Hyunmin Lee*
; Human IT Convergence Research Center, Korea Electronics Technology Institute, South Korea
Tai-Won Um
; Department of Cyber Security, Duksung Women's University, South Korea
Kwangki Kim
; School of IT Convergence, Korea Nazarene University, South Korea
Hye-Young Kim
; School of Game/Game Software, Hongik University, South Korea
Jinsul Kim*
; Department of ICT Convergence System Engineering, Chonnam National University, Gwangju, South Korea
Sažetak
In modern smart factories, quality estimation is vital for maximum productivity. However, quality estimation by definition relies on an imbalanced dataset, as most smart factories are highly efficient. In this research, we propose a guided quality estimation system that can recognize faulty data among a highly imbalanced production dataset. We also propose a customized LSTM model that is trained to ensure high accuracy in the quality estimation system. This is achieved by our proposed batch-wise balanced training method. Moreover, traditional means of evaluation for this type of method are not suitable, again due to the highly imbalanced nature of the dataset. Thus, a proper evaluation metric is also discussed. The proposed customized LSTM model with custom batch-wise SMOTE + ENN achieved 99.9% accuracy with an f1 score of 95%. This new proposed method for the imbalanced smart factory quality estimation will improve drastically and give pathway to more improved quality. Finally, we discuss practical implementation for the edge server consisting of the proposed guided production estimation system and real-time visualization. Feasibility analysis of this virtual environment-based application of the proposed framework ensured low computational overhead and faster processing.
Ključne riječi
data rebalancing; deep learning; ensemble Learning; industrial control; information management
Hrčak ID:
248214
URI
Datum izdavanja:
19.12.2020.
Posjeta: 1.650 *