Acknowledgement
This work was supported by Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (P0012744, The Competency Development Program for Industry Specialist), and was also supported by the research fund of Hanbat National University in 2020.
References
- Cheon, K.M. and Yang, J., An Ensemble Model for Machine Failure Prediction, J. Soc. Korea Ind. Syst. Eng., 2020, Vol. 43, No. 1, pp. 123-131. https://doi.org/10.11627/jkise.2020.43.1.123
- Cho, S.-J., Jun, H.-B., Shin, J.-H., and Hwang, H.-J., A Study on Estimating the Next Failure Time of a Compressor in LNG FPSO, J. Soc. Korea Ind. Syst. Eng., 2014, Vol. 37, No.4, pp. 12-23. https://doi.org/10.11627/jkise.2014.37.4.12
- Fang, W., Guo, Y., Liao, W., Ramani, K., and Huang, S., Big Data Driven Jobs Remaining Time Prediction in Discrete Manufacturing System : A Deep Learning-based Approach, International Journal of Production Research, 2020, Vol. 58, No. 9, pp. 2751-2766. https://doi.org/10.1080/00207543.2019.1602744
- Ghosh, A., Wang, G.-N., and Lee, J., A Novel Automata and Neural Network based Fault Diagnosis System for PLC Controlled Manufacturing Systems, Computers and Industrial Engineering, 2020, Vol. 139, pp. 1-16.
- Hong, K., Heo, J., Hwang, S., and Lee, J., Analysis of Sensor Data for Detecting the Abnormal State of FD Fan, Korean Journal of Computational Design and Engineering, 2016, Vol. 23, No. 2, pp. 137-143. https://doi.org/10.7315/CDE.2018.137
- Hwang, S., Heo, J., Hong, K., and Lee, J., Time Series Data Analysis and Fault Diagnosis of Plant Process Equipment Using Statistical Machine Learning Method, Korean Journal of Computational Design and Engineering, 2018, Vol. 23, No. 3, pp. 193-201. https://doi.org/10.7315/cde.2018.193
- Hwang, S., Kim, J., and Hwangbo, H., A Study of Sensor Data Analysis and Product Defect Improvement for Smart Factory, The Journal of Bigdata, 2018, Vol. 3, No. 1, pp. 95-103. https://doi.org/10.36498/kbigdt.2018.3.1.95
- Ioannides, M.G., Design and Implementation of PLC-based Monitoring Control System for Induction Motor, IEEE Transactions on Energy Conversion, 2004, Vol. 19, No. 3, pp. 469-476. https://doi.org/10.1109/TEC.2003.822303
- Jung, H. and Kim, J.-W., A Machine Learning Approach for Mechanical Motor Fault Diagnosis, J. Soc. Korea Ind. Syst. Eng., 2017, Vol. 40, No. 1, pp. 57-64. https://doi.org/10.11627/jkise.2017.40.1.057
- Jung, T.-L., Choi, S.-W., Yoo, W., and Kim, B.S., A Real Time Temperature Monitoring System for Plating Process, J. Soc. Korea Ind. Syst. Eng., 2015, Vol. 38, No. 4, pp. 72-79.
- Kim, S. and Ryu, K., An AR based Monitoring System for Discrete Manufacturing, Proceedings of 2018 Spring Korean Institute of Industrial Engineers, Gyeongju, Republic of Korea, 2018, pp. 707-712.
- Kwon, S., An, M., and Lee, H., Fault Detection and Classification of Process Cycle Signals Using Density-based Clustering and Deep Learning, Korean Institute of Industrial Engineers, 2018, Vol. 44, No. 6, pp. 475-482. https://doi.org/10.7232/JKIIE.2018.44.6.475
- Lee, K., Ku, R., Choi, S., Park, C., Park, S., and Wang, J., PLC Code Generation for the Control of Production System, Proceedings of 2008 Spring Korean Institute of Industrial Engineers, Gwangju, Republic of Korea, 2008, pp. 128-134.
- Miao, Y., Gowayyed, M., and Metze, F., EESEN : End-to-end Speech Recognition Using Deep RNN Models and WFST-Based Decoding, Proceedings of 2015 IEEE Workshop on Automatic Speech Recognition and Understanding, Scottsdale, AZ, U.S., 2015, pp. 167-174.
- Murata, T., Komoda, N., Matsumoto, K., and Haruna, K., A Petri Net-Based Controller for Flexible and Maintainable Sequence Control and its Applications in Factory Automation, IEEE Transactions on Industrial Electronics, 1986, Vol. IE-33, No. 1, pp. 1-8.
- Saez, M., Maturana, F.P., Barton, K., and Tilbury, D. M., Real-Time Manufacturing Machine and System Performance Monitoring Using Internet of Things, IEEE Transactions on Automation Science and Engineering, 2018, Vol. 15 No. 4, pp. 1735-1748. https://doi.org/10.1109/tase.2017.2784826
- Sameh, M., Tarek, A., and Yassine, K., Bearing and Rotor Faults detection and diagnosis of Induction Motors using Statistical Neural Networks, Proceedings of 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), Monastir, Tunisia, 2020, pp. 77-81.
- Selvin, S., Vinayakumar, R., Gopalakrishnan, E.A., Menon, V.K., and Soman, K.P., Stock Price Prediction Using LSTM, RNN and CNN-sliding Window Model, Proceedings of 2017 International Conference on Advances in Computing, Communications and Informatics, Udupi, India, 2017, pp. 1643-1647.
- Seong, K., Han, K., Pyun, J., Wang, G., and Park, S., PLC Program Monitoring for Manufacturing Systems Using PLC Signal Time Difference, Korean Journal of Computational Design and Engineering, 2009, Vol. 14, No. 3, pp. 176-185.
- Sherstinsky, A., Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network, Physica D : Nonlinear Phenomena, Vol. 404, pp. 1-28.
- Song, Y.-U. and Baek, S., Image Analysis based Data Pre-Processing of Digital Control Signals for Error Status Detection in Automatic Manufacturing System, Proceedings of The Korean Society of Mechanical Engineers, Pyeongchang, Republic of Korea, 2020, pp. 2075-2080.
- Vathsala, M.K. and Holi, G., RNN based Machine Translation and Transliteration for Twitter Data, International Journal of Speech Technology, 2020, Vol. 23, pp. 499-504. https://doi.org/10.1007/s10772-020-09724-9