Case study: the implementation of a data-driven industrial analytics methodology and platform for smart manufacturing
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Published
Nov 13, 2020
Peter O’Donovan
Ken Bruton
Dominic T.J. O’Sullivan
Abstract
Integrated, real-time and open approaches relating to the development of industrial analytics capabilities are needed to support smart manufacturing. However, adopting industrial analytics can be challenging due to its multidisciplinary and cross-departmental (e.g. Operation and Information Technology) nature. These challenges stem from the significant effort needed to coordinate and manage teams and technologies in a connected enterprise. To address these challenges, this research presents a formal industrial analytics methodology that may be used to inform the development of industrial analytics capabilities. The methodology classifies operational teams that comprise the industrial analytics ecosystem, and presents a technology agnostic reference architecture to facilitate the industrial analytics lifecycle. Finally, the proposed methodology is demonstrated in a case study, where an industrial analytics platform is used to identify an operational issue in a large-scale Air Handling Unit (AHU).
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Keywords
energy efficiency, Big Data, smart manufacturing, industrial analytics, air handling unit
References
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Zuehlke, D. (2010). SmartFactory—Towards a factory-of-things. Annual Reviews in Control, 34(1), 129–138. doi:10.1016/j.arcontrol.2010.02.008
Alves Santos, R., Normey-Rico, J. E., Merino Gómez, A., Acebes Arconada, L. F., & de Prada Moraga, C. (2005). OPC based distributed real time simulation of complex continuous processes. Simulation Modelling Practice and Theory, 13(7), 525–549. doi:10.1016/j.simpat.2005.01.005
Bagheri, B., Yang, S., Kao, H., & Lee, J. (2015). Cyber-physical Systems Architecture for Self-Aware Machines in Industry 4 . 0 Environment. IFAC PapersOnLine, 1622–1627. doi:10.1016/j.ifacol.2015.06.318
Brandl, D. (2008). What is ISA-95 ? Industrial Best Practices of Manufacturing Information Technologies with ISA-95 Models, 1–32.
Bruton, K., Raftery, P., O’Donovan, P., Aughney, N., Keane, M. M., & O’Sullivan, D. T. J. (2014). Development and alpha testing of a cloud based automated fault detection and diagnosis tool for Air Handling Units. Automation in Construction, 39(0), 70–83. doi:10.1016/j.autcon.2013.12.006
Cardiel, I. A., Gil, R. H., Somolinos, C. C., & Somolinos, J. C. (2012). Expert Systems with Applications A SCADA oriented middleware for RFID technology. Expert Systems With Applications, 39(12), 11115–11124. doi:10.1016/j.eswa.2012.03.045
Chand, S., & Davis, J. (2010). What is Smart Manufacturing ? Time Magazine.
Chungoora, N., Young, R. I., Gunendran, G., Palmer, C., Usman, Z., Anjum, N. a., … Case, K. (2013). A model-driven ontology approach for manufacturing system interoperability and knowledge sharing. Computers in Industry, 64(4), 392–401. doi:10.1016/j.compind.2013.01.003
Data Mining Group. (2016). PMML 4.3. Data Mining Group. Retrieved from http://dmg.org/pmml/v4-3/GeneralStructure.html
Davis, J., Edgar, T., Porter, J., Bernaden, J., & Sarli, M. (2012). Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Computers & Chemical Engineering, 47, 145–156. doi:10.1016/j.compchemeng.2012.06.037
Dworschak, B., & Zaiser, H. (2014). Competences for cyber-physical systems in manufacturing – first findings and scenarios. Procedia CIRP, 25, 345–350. doi:10.1016/j.procir.2014.10.048
Emerson, D., Kawamura, H., & Matthews, W. (2007). Plant-to-business interoperability using the ISA-95 standard.
Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How “big data” can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 1–13. doi:10.1016/j.ijpe.2014.12.031
Giovannini, A., Aubry, A., Panetto, H., Dassisti, M., & El Haouzi, H. (2012). Ontology-based system for supporting manufacturing sustainability. Annual Reviews in Control, 36(2), 309–317. doi:10.1016/j.arcontrol.2012.09.012
GitHub. (2016). OpenScoring. Retrieved from https://github.com/jpmml/openscoring
Gligor, A., & Turc, T. (2012). Development of a Service Oriented SCADA System. Procedia Economics and Finance, 3(12), 256–261. doi:10.1016/S2212-5671(12)00149-9
Hazen, B. T., Boone, C. a., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72–80. doi:10.1016/j.ijpe.2014.04.018
Heng, S. (2014). Industry 4.0: Huge potential for value creation waiting to be tapped. Deutsche Bank Research. Retrieved from http://www.dbresearch.com/servlet/reweb2.ReWEB?rwsite=DBR_INTERNET_EN-PROD&rwobj=ReDisplay.Start.class&document=PROD0000000000335628
Hong, X., & Jianhua, W. (2006). Using standard components in automation industry: A study on OPC Specification. Computer Standards & Interfaces, 28(4), 386–395. doi:10.1016/j.csi.2005.05.001
ISA. (2016a). ISA-88. Retrieved from http://www.isa-88.com
ISA. (2016b). ISA-95. Retrieved from http://www.isa-95.com
Jedliński, Ł., & Jonak, J. (2015). Early fault detection in gearboxes based on support vector machines and multilayer perceptron with a continuous wavelet transform. Applied Soft Computing, 30, 636–641. doi:10.1016/j.asoc.2015.02.015
Kastner, W., Neugschwandtner, G., Soucek, S., & Newman, H. M. (2005). Communication Systems for Building Automation and Control, 93(6).
Kumar, P., Dhruv, B., Rawat, S., & Rathore, V. S. (2014). Present and future access methodologies of big data. International Journal of Advance Research in Science and Engineering, 8354(3), 541–547.
Lee, J. (2014). Recent Advances and Transformation Direction of PHM. NIST. Retrieved August 3, 2015, from http://www.nist.gov/el/isd/upload/Keynote_Lee_IMS-University_of_Cincinnati_updated.pdf
Lee, J., Ardakani, H. D., Yang, S., & Bagheri, B. (2015). Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance & Service Innovation. Procedia CIRP, 38, 3–7. doi:10.1016/j.procir.2015.08.026
Lee, J., Bagheri, B., & Kao, H. (2015). A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3(September 2015), 18–23. doi:10.1016/j.mfglet.2014.12.001
Lee, J., Kao, H.-A. A., & Yang, S. (2014). Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment. Procedia CIRP, 16, 3–8. doi:10.1016/j.procir.2014.02.001
Lee, J., Lapira, E., Bagheri, B., & Kao, H. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 1(1), 38–41. doi:10.1016/j.mfglet.2013.09.005
Manufacturing, S., Manufacturing, C. S., Coalition, L., Smart, T., Leadership, M., Incorporated, E., … Any, D. (2011). Implementing 21st Century Smart Manufacturing.
MatrikonOPC. (2016). MatrikonOPC Simulation Server. Retrieved from https://www.matrikonopc.com/products/opc-drivers/opc-simulation-server.aspx
McKinsey. (2011). Big data : The next frontier for innovation , competition , and productivity.
Nagorny, K., Colombo, A. W., & Schmidtmann, U. (2012). A service and multi-agent-oriented manufacturing automation architecture. Computers in Industry, 63(8), 813–823. doi:10.1016/j.compind.2012.08.003
O’Donovan, P., Leahy, K., Bruton, K., & O’Sullivan, D. T. J. (2015). An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities. Journal of Big Data, 2(1), 25. doi:10.1186/s40537-015-0034-z
O’Donovan, P., Leahy, K., Bruton, K., & O’Sullivan, D. T. J. (2015). Big data in manufacturing: a systematic mapping study. Journal of Big Data, 2(1), 20. doi:10.1186/s40537-015-0028-x
Philip Chen, C. L., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314–347. doi:10.1016/j.ins.2014.01.015
Reinisch, C., Granzer, W., Praus, F., & Kastner, W. (2008). Integration of heterogeneous building automation systems using ontologies. 2008 34th Annual Conference of IEEE Industrial Electronics, 2736–2741. doi:10.1109/IECON.2008.4758391
Samad, T., & Frank, B. (2007). Leveraging the web: a universal framework for building automation. In Proceedings of the 2007 American Control Conference.
Scholten, B. (2007). Integrating ISA-88 and ISA-95. In ISA Expo (pp. 2–4).
Sharma, P., & Sharma, M. (2014). Artificial Intelligence in Advance Manufacturing Technology-A Review Paper on Current Application. International Journal of Engineering, Management & Sciences, 1(1), 4–7.
Vera-baquero, A., Colomo-palacios, R., & Molloy, O. (2014). Towards a process to guide Big Data based Decision Support Systems for Business Processes. In Conference on ENTERprise Information Systems Towards (Vol. 00).
Vincent Wang, X., & Xu, X. W. (2013). An interoperable solution for Cloud manufacturing. Robotics and Computer-Integrated Manufacturing, 29(4), 232–247. doi:10.1016/j.rcim.2013.01.005
Weiss, B. A., & Donmez, M. A. (2014). Standards Related to Prognostics and Health Management (PHM ) for Manufacturing Standards Related to Prognostics and Health Management (PHM ) for Manufacturing. In Annual Conference of the Prognostics and Health Management Society. doi:10.6028/NIST.IR.8012
Wright, P. (2014). Cyber-physical product manufacturing. Manufacturing Letters, 2(2), 49–53. doi:10.1016/j.mfglet.2013.10.001
Wu, D., Greer, M. J., Rosen, D. W., & Schaefer, D. (2013). Cloud manufacturing: Strategic vision and state-of-the-art. Journal of Manufacturing Systems, 32(4), 564–579. doi:10.1016/j.jmsy.2013.04.008
Xu, X. (2012). From cloud computing to cloud manufacturing. Robotics and Computer-Integrated Manufacturing, 28(1), 75–86. doi:10.1016/j.rcim.2011.07.002
Zuehlke, D. (2010). SmartFactory—Towards a factory-of-things. Annual Reviews in Control, 34(1), 129–138. doi:10.1016/j.arcontrol.2010.02.008
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