在科技的發展與輔助之下，智慧製造與智慧工廠的概念逐漸普及，傳統製造業多積極啟動了數位轉型並投入到智慧製造的行列之中，而分析這些數位轉型過程中所收集之數據能發揮重要的作用；而近年新冠疫情的持續延燒加速了中小型工廠進行數位轉型，在減少人力需求的前提下，工廠現場亦亟需一套資訊系統來監控並穩定生產過程。本研究提出一套資料收集與監控的系統，並且能整合多台生產設備，此一系統可收集並顯示高頻率的生產資料，也能長時間且持續不間斷地進行設備監控以符合實際應用所需。此外，我們更採用了OPC UA通訊協定來整合所有的生產資訊，此一通訊協定為開源、跨平台，並為自動化技術所設計的機器對機器網路通訊協定，也已被多個國際組織所採用；具體而言，我們在每台生產機器上放入OPC UA伺服器，並定義節點以記載的關鍵生產訊息，而中心控管處透過聚合式的OPC UA客戶端來收集所有伺服器上的資訊後，管理者便可透過儀表板來監控生產過程。以往在生產過程中若遇到問題，往往只能憑著現場人員的經驗來處理，而無法系統化、結構化的解釋，經過數位轉型的過程後，我們可掌握更多的生產數據，藉此期望能降低人工作業的錯誤率、提高決策正確率，以及更好地達到品質控制，甚至是品質預測與設備預知保養。

With the development and assistance of technology, the concept of smart manufacturing and smart factories is gradually becoming popular in the manufacturing industry. The manufacturing industry has actively initiated digital transformation and is engaged in smart manufacturing, and analyzing the data collected during digital transformations can play an important role. In recent years, the continuous spread of the COVID-19 epidemic has accelerated small and medium-sized factories engaged in digital transformation. Due to the demand for reduced manpower, the factories urgently need a system to monitor and stabilize the production process. This study proposes a data collection and monitoring system that can integrate multiple production equipment, this system can collect and display high-frequency production data, and also allow for long and continuous equipment monitoring to meet the needs of the actual application. In addition, we adopt OPC UA to integrate all the production information, which is an open-source, cross-platform, machine-to-machine network communication protocol that is designed for automation technology and has been adopted by serval international organizations. Specifically, we establish OPC UA servers on each production machine and define nodes to record key production information. The central control office gathers all the information from the servers through an aggregated OPC UA client, and then the manager can monitor the production process through dashboard. In the past, when problems encountered in the production process, they could only be dealt with by the experience of the field staff and could not be explained systematically and structurally. After the digital transformation, we can grasp more production data to reduce the error rate of human work, improve the correct decision rate, and achieve better quality control, even quality prediction and equipment prediction, and maintenance.

[1] Narayanamurthy, G., and Tortorella, G., “Impact of COVID-19 outbreak on employee performance–moderating role of industry 4.0 base technologies,” International Journal of Production Economics, 234, article 108075, April 2021.
[2] Shao, C., Paynabar, K., Kim, T. H., Jin, J. J., Hu, S. J., Spicer, J. P., Wang, H., and Abell, J. A., “Feature selection for manufacturing process monitoring using cross-validation,” Journal of Manufacturing Systems, 32(4), pages 550-555, October 2013.
[3] Schuh, G., Anderl, R., Dumitrescu, R., Krüger, A., and ten Hompel, M., “Industrie 4.0 maturity index. Managing the digital transformation of companies,” acatech STUDY, 64, April 2020.
[4] C.-L Hung, C,-H Chang, W,-J Lin, “The Critical Technologies of Industry 4.0 and Smart Manufacturing: Industrial Internet of Things and Artificial Intelligence,” Instruments Today, 221, pages 19-25, December 2019.
[5] Deshmukh, S. G., and Haleem, A., “Framework for manufacturing in post-COVID-19 world order: An Indian perspective,” International Journal of Global Business and Competitiveness, 15(1), pages 49-60, July 2020.
[6] Reis, M. S., and Gins, G., “Industrial process monitoring in the big data/industry 4.0 era: From detection, to diagnosis, to prognosis,” Processes, 5(3), article 35, June 2017.
[7] Ge, Z., and Chen, J., “Plant-wide industrial process monitoring: A distributed modeling framework,” IEEE Transactions on Industrial Informatics, 12(1), pages 310-321, February 2016.
[8] Choi, S. W., and Lee, I. B., “Multiblock PLS-based localized process diagnosis,” Journal of Process Control, 15(3), pages 295-306, April 2005.
[9] Alphonsus, E. R., & Abdullah, M. O., “A review on the applications of programmable logic controllers (PLCs),” Renewable and Sustainable Energy Reviews, 60, pages 1185-1205, July 2016.
[10] Rullan, A., “Programmable logic controllers versus personal computers for process control,” Computers & industrial engineering, 33(1-2), pages 421-424, October 1997.
[11] Programmable Logic Controllers (PLCs): Basics, Types & Applications | Programmable Logic Controller, https://www.tsca.com.ph/programmable-logic-controllers-plcs-basics-types-applications-programmable-logic-controller/.
[12] Daneels, A., and Salter, W., “What is SCADA?,” International Conference on Accelerator and Large Experimental Physics Control Systems, 1999.
[13] Shahzad, A., Musa, S., Aborujilah, A., and Irfan, M., “The SCADA review: system components, architecture, protocols and future security trends,” American Journal of Applied Sciences, 11(8), pages1418-1425, June 2014.
[14] Capodaglio, A. G., Callegari, A., and Molognoni, D., “Online monitoring of priority and dangerous pollutants in natural and urban waters: a state-of-the-art review,” Management of Environmental Quality: An International Journal, 27(5), pages 507-536, August 2016.
[15] Ren, L., Xie, S., Zhang, Y., Peng, J., and Zhang, L., “Chattering analysis for discrete sliding mode control of distributed control systems,” Journal of Systems engineering and Electronics, 27(5), pages 1096-1107, October 2016.
[16] Belapurkar, R., Yedavalli, R., and Moslehi, B., “Stability of fiber optic networked decentralized distributed engine control under time delays,” 45th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, page 4885, 2009.
[17] Cloosterman, M. B., Hetel, L., Van de Wouw, N., Heemels, W. P. M. H., Daafouz, J., and Nijmeijer, H., “Controller synthesis for networked control systems,” Automatica, 46(10), pages1584-1594, October 2010.
[18] Sebastian-Coleman, L., “Measuring data quality for ongoing improvement: a data quality assessment framework,” 2012.
[19] Functional Levels of a manufacturing control operation, https://plcscadaacademy.blogspot.com/2020/03/functional-levels-of-manufacturing.html.
[20] Zhang, W., and Liu, Y., “Model Validation of Control Systems With an Application in Abnormal Driving State Detection,” Model Engineering for Simulation, pages 419-429, 2019.
[21] DIAView SCADA, https://www.deltaww.com/zh-TW/products/DIAView-SCADA-System/3166/.
[22] SIMATIC WinCC V7, https://new.siemens.com/global/en/products/automation/industry-software/automation-software/scada/simatic-wincc-v7.html.
[23] Advantech WebAccess/SCADA - IIoT Application Software Framework, https://www.advantech.tw/industrial-automation/webaccess/webaccessscada.
[24] Ioana, A., and Korodi A., “DDS and OPC UA Protocol Coexistence Solution in Real-Time and Industry 4.0 Context Using Non-Ideal Infrastructure,” Sensors, 21(22), article 7760, November 2021.
[25] Geng, D., Zhang, C., Xia, C., Xia, X., Liu, Q., and Fu, X., “Big data-based improved data acquisition and storage system for designing industrial data platform,” IEEE Access, 7, pages 44574-44582, April 2019.
[26] O'Neil, C., and Schutt, R., “Doing data science: Straight talk from the frontline,” 2013.
[27] Stavropoulos, P., Chantzis, D., Doukas, C., Papacharalampopoulos, A., and Chryssolouris, G., “Monitoring and control of manufacturing processes: A review,” Procedia CIRP, 8, pages 421-425, 2013.
[28] Snee, R. D., “Crucial considerations in monitoring process performance and product quality,” Pharmaceutical Technology, 34(10), pages 38-40, 2010.