Abstract
Digital transformation and evolution of integrated computational and visualisation technologies lead to new opportunities for reinforcing knowledge-based maintenance through collection, processing and provision of actionable information and recommendations for maintenance operators. Providing actionable information regarding both corrective and preventive maintenance activities at the right time may lead to reduce human failure and improve overall efficiency within maintenance processes. Selecting appropriate digital assistance systems (DAS), however, highly depends on hardware and IT infrastructure, software and interfaces as well as information provision methods such as visualization. The selection procedures can be challenging due to the wide range of services and products available on the market. In particular, underlying machine learning algorithms deployed by each product could provide certain level of intelligence and ultimately could transform diagnostic maintenance capabilities into predictive and prescriptive maintenance. This paper proposes a process-based model to facilitate the selection of suitable DAS for supporting maintenance operations in manufacturing industries. This solution is employed for a structured requirement elicitation from various application domains and ultimately mapping the requirements to existing digital assistance solutions. Using the proposed approach, a (combination of) digital assistance system is selected and linked to maintenance activities. For this purpose, we gain benefit from an in-house process modeling tool utilized for identifying and relating sequence of maintenance activities. Finally, we collect feedback through employing the selected digital assistance system to improve the quality of recommendations and to identify the strengths and weaknesses of each system in association to practical usecases from TU Wien Pilot-Factory Industry 4.0.
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Ansari, F.: Meta-Analysis of Knowledge Assets for Continuous Improvement of Maintenance Cost Controlling. Faculty of Science and Technology, University of Siegen (2014).
Nemeth, T., Ansari, F., Sihn, W., Haslhofer, B., Schindler, A.: PriMa-X: A Reference Model for Realizing Prescriptive Maintenance and Assessing its Maturity Enhanced by Machine Learning. Procedia CIRP, Vol. 72, pp. 1039-1044. (2018).
Glawar, R., Karner, M., Nemeth, T., Matyas, K., Sihn, W.: An Approach for the Integration of Anticipative Maintenance Strategies within a Production Planning and Control Model. Procedia CIRP 67 46 – 51, (2018).
Hao, Y., & Helo, P.: The role of wearable devices in meeting the needs of cloud manufacturing: A case study. Robotics and Computer-Integrated Manufacturing, 45. Jg., S. 168-179. (2017).
Kernchen, A., Jachmann, D., Adler, S..: Assistenzsysteme für die Instandhaltung und Störungsbehebung. 21. Magdeburger Logistik Tage. Logistik neu denken und gestalten. S.195. (2016).
Niemöller, C., Metzger, D., Fellmann, M., Özcan, D., Thomas, O.: Shaping the future of mobile service support systems-ex-ante evaluation of smart glasses in technical customer service processes. Informatik 2016, (2016).
Erkoyuncu, J. A., del Amo, I. F., Dalle Mura, M., Roy, R., Dini, G.: Improving efficiency of industrial maintenance with context aware adaptive authoring in augmented reality. CIRP Annals 66.1. 465-468. (2017).
Uhlmann E., Raue N., Geisert C.: Unterstützungspotenziale der Automatisierungstechnik im technischen Kundendienst. Summary of an explorative survey on best pactices in field service. Berlin: Fraunhofer IPK, (2013).
Mourtzis, D., Zogopoulos, V., Vlachou, E.: Augmented reality application to support remote maintenance as a service in the Robotics industry. Procedia CIRP 63: 46-51. (2017).
Neges, M., Wolf, M., Abramovici, M.: Secure access augmented reality solution for mobile maintenance support utilizing condition-oriented work instructions. Procedia CIRP, 38, 58-62. (2015).
Palmarini, R., Erkoyuncu, J., Rajkumar, R..: An innovative process to select Augmented Reality (AR) technology for maintenance. Procedia CIRP 59: 23-28 (2017).
Palmarini, R., Erkoyuncu, J. A., Roy, R., Torabmostaedi, H.: A systematic review of augmented reality applications in maintenance. Robotics and Computer-Integrated Manufacturing 49: 215- 228. (2018).
Hold, P., Erol, S., Reisinger, G., & Sihn, W.: Planning and Evaluation of Digital Assistance Systems. Procedia Manufacturing 9:143-150. (2017).
Reisinger, G., Komenda, T., Hold, P., & Sihn, W.: A Concept towards Automated Data-Driven Reconfiguration of Digital Assistance Systems. Education & Training 2351: 9789. (2018).
Hohwieler E, Geisert C.: Intelligent Machines Offer Condition Monitoring and Maintenance Prediction Services. In: Teti R, editor. Proceedings of the 4th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’04). 30 June - 2 July 2004, Sorrento, Italy; pp. 599-604. (2004).
Hohwieler E, Berger R, Geisert C.: Condition Monitoring Services for e-Maintenance. In: Zaremba M, Sasiadek J, Erbe HH, editors. A proceedings volume from the 7th IFAC Symposium, Gatineau, Québec, Canada, 6-9 June 2004. Oxford: Elsevier pp. 239-244. (2005).
Ziegler, J., Heinze, S., Urbas, L.: The potential of smartwatches to support mobile industrial maintenance tasks. Emerging Technologies & Factory Automation (ETFA), IEEE 20th Conference on. IEEE, (2015).
Bokrantz, J., Skoogh, A., Berlin, C., & Stahre, J.: Maintenance in digitalised manufacturing: Delphi- based scenarios for 2030. International Journal of Production Economics, 191, 154-169. (2017).
Hold, P., Ranz, F., Hummel, V., Sihn, W..: Durchblick im Variantendschungel: visuelle Assistenzsysteme als Flexibilitätshebel auf dem Shop Floor (2015).
20. Ritchey, T.: Modeling alternative futures with general morphological analysis. World Future Review, 3(1), 83-94. (2011).
Ritchey, T.: Problem structuring using computer-aided morphological analysis. Journal of the Operational Research Society, 57(7), 792-801. (2006).
Im, K., Cho, H.: A systematic approach for developing a new business model using morphological analysis and integrated fuzzy approach. Expert Systems with Applications, 40(11), 4463-4477. (2013).
Spur, G.; Mertins, K.; Jochem, R.: Integrated Enterprise Modelling. Berlin, Wien, Zürich: Beuth. (1996).
Mertins K, Jaekel FW.: MO2GO: User Oriented Enterprise Models for Organisational and IT Solutions. In: Schmidt G, Mertins K, Bernus P, editors. Handbook on architectures of information systems. Berlin, New York: Springer p. 649-663. (2006).
Uhlmann, E.; Geisert, C.; Raue, N.; Gabriel, C.: Situation Adapted Field Service Support Using Business Process Models and ICT Based Human-Machine-Interaction. Procedia CIRP 47, p. 240- 245. (2016).
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Kovacs, K., Ansari, F., Geisert, C., Uhlmann, E., Glawar, R., Sihn, W. (2019). A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance. In: Beyerer, J., Kühnert, C., Niggemann, O. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation, vol 9. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58485-9_10
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