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Original Articles

PriMa: a prescriptive maintenance model for cyber-physical production systems

ORCID Icon, &
Pages 482-503 | Received 30 May 2018, Accepted 09 Jan 2019, Published online: 20 Feb 2019

References

  • Aamodt, A., and E. Plaza. 1994. “Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches.” AI Communications 7 (1): 39–59.
  • Accorsi, R., R. Manzini, P. Pascarella, M. Patella, and S. Sassi. 2017. “Data Mining and Machine Learning for Condition-Based Maintenance.” Procedia Manufacturing 11: 1153–1161. doi:10.1016/j.promfg.2017.07.239.
  • Aizpurua, J. I., V. M. Catterson, Y. Papadopoulos, F. Chiacchio, and D. D’Urso. 2017. “Supporting Group Maintenance through Prognostics-Enhanced Dynamic Dependability Prediction.” Reliability Engineering & System Safety 168: 171–188. doi:10.1016/j.ress.2017.04.005.
  • Ansari, F. 2014. “Meta-Analysis of Knowledge Assets for Continuous Improvement of Maintenance Cost Controlling.” PhD Diss., Germany: University of Siegen.
  • Ansari, F., M. Khobreh, U. Seidenberg, and W. Sihn. 2018. “A Problem-Solving Ontology for Human-Centered Cyber Physical Production Systems.” CIRP Journal of Manufacturing Science and Technology 22C: 91–106. Elsevier. doi:10.1016/j.cirpj.2018.06.002.
  • Ansari, F., P. Hold, W. Mayrhofer, S. Schlund, and 2018. AUTODIDACT: Introducing the Concept of Mutual Learning into a Smart Factory Industry 4.0, In Proceedings of 15th International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2018), October 21–23, Budapest, Hungary, 61–68.
  • Ansari, F., P. Uhr, and M. Fathi. 2014. “Textual Meta-Analysis of Maintenance Management’s Knowledge Assets.” International Journal of Services, Economics and Management, Inderscience Enterprises Ltd. 6: 14–37.
  • Ansari, F., and R. Glawar. 2018. Knowledge Based Maintenance, Instandhaltungslogistik - Qualität Und Produktivität Steigern (Maintenance Logistics – Enhancing Quality and Productivity). edited by K. Matyas. 7th ed., 318–342. Munich, Germany: Carl Hanser Verlag GmbH & Co. KG.
  • Ansari, F., R. Glawar, and W. Sihn. 2017. Prescriptive Maintenance of CPPS by Integrating Multimodal Data with Dynamic Bayesian Networks, Machine Learning for Cyber Physical Systems. Springer. (In Press).
  • Ansari, F., S. Erol, and W. Sihn. 2018. “Rethinking Human-Machine Learning in Industry 4.0: How Does the Paradigm Shift Treat the Role of Human Learning?” Procedia Manufacturing 23C: 117–122. doi:10.1016/j.promfg.2018.04.003.
  • Baars, H., and H. G. Kemper. 2008. Management Support with Structured and Unstructured Data: An Integrated Business Intelligence Framework, Information Systems Management, 132–148. Vol. 25. London, UK: Taylor& Francis Group.
  • Beierle, C., and G. Kern-Isberner. 2008. Methoden wissensbasierter Systeme. 4th ed. Berlin, Germany: Springer.
  • Belyi, D., E. Popova, D. P. Morton, and P. Damien. 2017. “Bayesian Failure-Rate Modeling and Preventive Maintenance Optimization.” European Journal of Operational Research 262 (3): 1085–1093. doi:10.1016/j.ejor.2017.04.019.
  • Benttaleb, M., F. Hnaien, and F. Yalaoui. 2016. “Two-Machine Job Shop Problem for Makespan Minimization under Availability Constraint.” IFAC-PapersOnLine 49 (28): 132–137. doi:10.1016/j.ifacol.2016.11.023.
  • Biedermann, H. 2014. Anlagenmanagement im Zeitalter von Industrie 4.0 - Handlungsfelder für die industrielle Instandhaltung Instandhaltung im Wandel (Maintenace in Transition), 23–32. Cologne, Germany: TÜV Rheinland Group.
  • Bousdekis, A., B. Magoutas, D. Apostolou, and G. Mentzas. 2015. “Review, Analysis and Synthesis of Prognostic-Based Decision Support Methods for Condition Based Maintenance.” Journal of Intelligent Manufacturing 1–14.
  • Bumblauskas, D., D. Gemmill, A. Igou, and J. Anzengruber. 2017. “Smart Maintenance Decision Support Systems (SMDSS) Based on Corporate Big Data Analytics.” Expert Systems with Applications 90: 303–317. doi:10.1016/j.eswa.2017.08.025.
  • Chapman, P., J. Clinton, R. Kerber, T. Khabaza, T. Reinartz, C. Shearer, and R. Wirth. 2000. Crisp-Dm 1.0. CRISP-DM Consortium, 76 (3).
  • CPS Summit. 2008. “Report: Cyber-Physical Systems Summit, 2008.” Technical Report. Accessed January 2 2019. http://cra.org/ccc/wp-content/uploads/sites/2/2015/05/CPS_Summit_Report.pdf
  • Denkena, B., M. Shanib, J. Damm, and B. Bergmann. 2017. “Selbstparametrierende Prozessüberwachungssysteme: Selbstanpassende Prozessüberwachungsstrategien Für Die Serienfertigung (Self-Parameterising Process Monitoring Systems - Self-Adjusting Process Monitoring Strategies for Series Production).” Wt-Online 7/8: 487–491.
  • DIN. 2012. DIN 31051:2012-09:Fundamentals of Maintenance. Berlin, Germany: Beuth Verlag.
  • El Khoukhi, F., J. Boukachour, and A. E. H. Alaoui. 2017. “The “Dual-Ants Colony”: A Novel Hybrid Approach for the Flexible Job Shop Scheduling Problem with Preventive Maintenance.” Computers & Industrial Engineering 106: 236–255. doi:10.1016/j.cie.2016.10.019.
  • Engel, G., T. Greiner, and S. Seifert. 2018. “Ontology-Assisted Engineering of Cyber-Physical Production Systems in the Field of Process Technology.” IEEE Transactions on Industrial Informatics 14 (6): 2792–2802. doi:10.1109/TII.2018.2805320.
  • Famurewa, S. M., L. Zhang, and M. Asplund. 2017. “Maintenance Analytics for Railway Infrastructure Decision Support.” Journal of Quality in Maintenance Engineering 23 (3): 310–325. doi:10.1108/JQME-11-2016-0059.
  • Fitouri, C., N. Fnaiech, C. Varnier, F. Fnaiech, and N. Zerhouni. 2016. “A Decison-Making Approach for Job Shop Scheduling with Job Depending Degradation and Predictive Maintenance.” IFAC-PapersOnLine 49 (12): 1490–1495. doi:10.1016/j.ifacol.2016.07.782.
  • Fnaiech, N., C. Fitouri, C. Varnier, F. Fnaiech, and N. Zerhouni. 2015. “A New Heuristic Method for Solving Joint Job Shop Scheduling of Production and Maintenance.” IFAC-PapersOnLine 48 (3): 1802–1808. doi:10.1016/j.ifacol.2015.06.348.
  • Franciosi, C., A. Lambiase, and S. Miranda. 2017. “Sustainable Maintenance: A Periodic Preventive Maintenance Model with Sustainable Spare Parts Management.” IFAC-PapersOnLine 50 (1): 13692–13697. doi:10.1016/j.ifacol.2017.08.2536.
  • Glawar, R., C. Habersohn, T. Nemeth, K. Matyas, B. Kittl, and W. Sihn. 2016a. “A Holistic Approach for Anticipative Maintenance Planning Supported by A Dynamic Calculation of Wear Reserve.” Journal of Maintenance Engineering 1: 313–324.
  • Glawar, R., M. Karner, T. Nemeth, K. Matyas, and W. Sihn. 2018. “An Approach for the Integration of Anticipative Maintenance Strategies within a Production Planning and Control Model.” Procedia CIRP 67C: 46–51. doi:10.1016/j.procir.2017.12.174.
  • Glawar, R., Z. Kemeny, T. Nemeth, K. Matyas, L. Monostori, and W. Sihn. 2016b. “A Holistic Approach for Quality Oriented Maintenance Planning Supported by Data Mining Methods.” Procedia CIRP 57: 259–264. doi:10.1016/j.procir.2016.11.045.
  • Gruber, T. 1993. “A Translation Approach to Portable Ontology Specifications.” Knowledge Acquisition 5 (2): 199–220. doi:10.1006/knac.1993.1008.
  • Guarino, N., and P. Giaretta. 1995. Ontologies and Knowledge Bases Towards a Terminological Clarification, Towards Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing, 25–32.  Amsterdam, the Netherlands: IOS Press.
  • IoT Analytics. 2017. “Predictive Maintenance Market Report 2017–22.” Market Report. Hamburg, Germany: IoT Analytics.
  • Kagermann, H., W. Wahlster, and J. Helbig. 2013. Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0. Final Report of the INDUSTRIE 4.0 Working Group. Edited by Forschungsunion Wirtschaft – Wissenschaft und acatech. Munich, Germany: Deutsche Akademie der Technikwissenschaften e.V.
  • Karim, R., J. Westerberg, D. Galar, and U. Kumar. 2016. “Maintenance Analytics–The New Know in Maintenance.” IFAC-PapersOnLine 49 (28): 214–219. doi:10.1016/j.ifacol.2016.11.037.
  • Kelleher, J. D., B. Mac Namee, and A. D’Arcy. 2015. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies, 1–3, 12–15, 17, 117, 153–158, 179, 247, 323. Cambridge, USA: MIT Press.
  • Klahold, A., P. Uhr, F. Ansari, and M. Fathi. 2013. “Using Word Association to Detect Multi-Topic Structures in Text Documents.” IEEE Intelligent Systems. 29 (5): 40–46. IEEE Computer Society. doi:10.1109/MIS.2013.120.
  • Kovacs, K., F. Ansari, C. Geisert, E. Uhlmann, R. Glawar, and W. Sihn. 2019. A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance, Machine Learning for Cyber Physical Systems, 87–96. Berlin, Heidelberg, Germany: Springer.
  • Lee, E. A. 2017. What Is Real Time Computing? A Personal View. IEEE Design & Test.
  • Lee, E. A., and S. A. Seshia. 2015. Introduction to Embedded Systems, A Cyber-Physical Systems Approach. 2nd ed. Cambridge, USA: MIT Press, ISBN 978-0-262-53381-2, 2017.
  • Legat, C., C. Seitz, S. Lamparter, and S. Feldmann. 2014. “Semantics to the Shop Floor: Towards Ontology Modularization and Reuse in the Automation Domain.” IFAC Proceedings 47 (3): 3444–3449. doi:10.3182/20140824-6-ZA-1003.02512.
  • Li, J. Q., Q. K. Pan, and M. F. Tasgetiren. 2014. “A Discrete Artificial Bee Colony Algorithm for the Multi-Objective Flexible Job-Shop Scheduling Problem with Maintenance Activities.” Applied Mathematical Modelling 38 (3): 1111–1132. doi:10.1016/j.apm.2013.07.038.
  • Liao, W., and T. Wang. 2018. “An Optimization Approach Using in Production Scheduling with Different Order.”  In Industrial Technology and Management (ICITM), 2018 7th International Conference on, 237-241. IEEE.
  • Linstedt, D., and M. Olschimke. 2015. Building a Scalable Data Warehouse with Data Vault 2.0, 17–32, 89–121. 1st ed. Burlington, Massachusetts, USA: Morgan Kaufmann.
  • Lueth, K. L., C. Patsioura, Z. D. Williams, and Z. Z. Kermani. 2016. “Industrial Analytics 2016/2017: The Current State of Data Analytics Usage in Industrial Companies.” Technical Report. IoT Analytics.
  • Maier, R. 2009. Knowledge Management Systems: Information and Communication Technologies for Knowledge Management. Berlin, Germany: Springer.
  • Matyas, K., T. Nemeth, K. Kovacs, and R. Glawar. 2017. “A Procedural Approach for Realizing Prescriptive Maintenance Planning in Manufacturing Industries.” CIRP Annals - Manufacturing Technology 66 (1): 461–464. doi:10.1016/j.cirp.2017.04.007. doi:10.1016/j.cirp.2017.04.007.
  • Miebach, T., M. Schmidt, and P. Nyhuis. 2017. “Das Intelligente Instandhaltungssystem: Ein Ansatz Zur Selbstlernenden Gestaltung Von Reaktionsbibliotheken (The Intelligent Maintenance System - an Approach for the Self-Learning Structuring of Maintenance Libraries).” Wt-Online 7/8: 530–535.
  • Mokhtari, H., and A. Hasani. 2017. “An Energy-Efficient Multi-Objective Optimization for Flexible Job-Shop Scheduling Problem.” Computers & Chemical Engineering 104: 339–352. doi:10.1016/j.compchemeng.2017.05.004.
  • Mokhtari, H., and M. Dadgar. 2015. “Scheduling Optimization of a Stochastic Flexible Job-Shop System with Time-Varying Machine Failure Rate.” Computers & Operations Research 61: 31–45. doi:10.1016/j.cor.2015.02.014.
  • Monostori, L., B. Kádár, T. Bauernhansl, S. Kondoh, S. Kumara, G. Reinhart, O. Sauer, G. Schuh, W. Sihn, and K. Ueda. 2016. “Cyber-Physical Systems in Manufacturing.” CIRP Annals-Manufacturing Technology 65 (2): 621–641. doi:10.1016/j.cirp.2016.06.005.
  • Mourtzis, D., N. Boli, and S. Fotia. 2017. “Knowledge-Based Estimation of Maintenance Time for Complex Engineered-To-Order Products Based on KPIs Monitoring: A PSS Approach.” Procedia CIRP 63 (1): 236–241. doi:10.1016/j.procir.2017.03.317.
  • Nasiri, S., M. R. Khosravani, and K. Weinberg. 2017. “Fracture Mechanics and Mechanical Fault Detection by Artificial Intelligence Methods: A Review.” Engineering Failure Analysis 81: 270–293. doi:10.1016/j.engfailanal.2017.07.011.
  • Nemeth, T., F. Ansari, W. Sihn, B. Haslhofer, and A. Schindler. 2018. “PriMa-X: A Reference Model for Realizing Prescriptive Maintenance and Assessing Its Maturity Enhanced by Machine Learning.” Procedia CIRP 72: 1039–1044. doi:10.1016/j.procir.2018.03.280.
  • North, K., and R. Maier. 2018. "Wissen 4.0–Wissensmanagement Im Digitalen Wande"l. HMD Praxis der Wirtschaftsinformatik, 1–17. Berlin, Germany: Springer.
  • Otto, B., S. Auer, J. Cirullies, J. Jürjens, N. Menz, J. Schon, and S. Wenzel, Industrial Data Space: Digital Souvereignity over Data, Fraunhofer White Paper, 2016.
  • Pawellek, G. 2013. Integrierte Instandhaltung und Ersatzteillogistik: Vorgehensweisen, Methoden, Tools. Berlin, Germany: Springer.
  • Perner, P. 2008. Case-Based Reasoning on Images and Signals, Studies in Computational Intelligence. Vol. 37. Berlin, Germany: Springer.
  • Poon, T. C., K. L. Choy, H. K. Chow, H. C. Lau, F. T. Chan, and K. C. Ho. 2009. “A RFID Case-Based Logistics Resource Management System for Managing Order-Picking Operations in Warehouses.” Expert Systems with Applications 36 (4): 8277–8301. doi:10.1016/j.eswa.2008.10.011.
  • Rahmati, S. H. A., A. Ahmadi, and B. Karimi. 2018. “Multi-Objective Evolutionary Simulation Based Optimization Mechanism for a Novel Stochastic Reliability Centered Maintenance Problem.” Swarm and Evolutionary Computation 40: 255–271. doi:10.1016/j.swevo.2018.02.010.
  • Reiner, J., J. Koch, I. Krebs, S. Schnabel, and T. Siech. 2005. Knowledge Management Issues for Maintenance of Automated Production Systems, Integrating Human Aspects in Production Management, IFIP International - Federation for Information Processing. Vol. 160. Berlin, Germany: Springer.
  • Reuss, P., R. Stram, K. D. Althoff, W. Henkel, and F. Henning. 2018. Knowledge Engineering for Decision Sup-Port on Diagnosis and Maintenance in the Aircraft Domain, Synergies between Knowledge Engi-Neering and Software Engineering, 173–196. Berlin, Germany: Springer.
  • Ruschel, E., E. A. P. Santos, and E. D. F. R. Loures. 2017. “Mining Shop-Floor Data for Preventive Maintenance Management: Integrating Probabilistic and Predictive Models.” Procedia Manufacturing 11: 1127–1134. doi:10.1016/j.promfg.2017.07.234.
  • Russell, S., and P. Norvig. 2010. Artificial Intelligence: A Modern Approach. USA: Prentice Hall.
  • Schiuma, G. 2009. “The Managerial Foundations of Knowledge Assets Dynamics.” Knowledge Management Research & Practice 7 (4): 290–299. doi:10.1057/kmrp.2009.21.
  • Schmidt, B., L. Wang, and D. Galar. 2017. “Semantic Framework for Predictive Maintenance in a Cloud Environment.” Procedia CIRP 62: 583–588. doi:10.1016/j.procir.2016.06.047.
  • Seidenberg, U., and F. Ansari. 2017. “Qualitätsmanagement in Der Additiven Fertigung – Herausforderungen Und Handlungsempfehlungen (Quality Management in Additive Manufacturing - Challenges and Recommendations for Action).” In 3D-Printing: Recht, Wirtschaft und Technik des industriellen 3D-Drucks, Handbook, edited by A. Leupold and S. Glossner, 159–214, Munich, Germany, Verlag C.H.Beck.
  • Sekulić, M., P. Kovač, M. Gostimirović, M. Hadžistević, and Z. Jurković. 2014. “Prediction of the Main Cutting Force in Drilling by Kienzle Equation.” Journal of Trends in the Development of Machinery and Associated Technology 18 (1): 27.
  • Shamsaei, F., and M. Van Vyve. 2017. “Solving Integrated Production and Condition-Based Maintenance Planning Problems by MIP Modeling.” Flexible Services and Manufacturing Journal 29 (2): 184–202. doi:10.1007/s10696-016-9244-8.
  • Studer, R., V. Benjamins, and D. Fensel. 1998. “Knowledge Engineering: Principles and Methods.” Data and Knowledge Engineering 25 (1): 161–197. doi:10.1016/S0169-023X(97)00056-6.
  • Sturm, A. 2001. Wissen basierte Betriebsführung und Instandhaltung. Essen: VGB PowerTech Service GmbH.
  • Ullrich, C. 2016. “An Ontology for Learning Services on the Shop Floor.” In 13th International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2016), 17–24. Mannheim, Germany: International Association for Development of the Information Society, October 28–30.
  • Wagner, T., C. Herrmann, and S. Thiede. 2017. “Industry 4.0 Impacts on Lean Production Systems.” Procedia CIRP 63: 125–131. doi:10.1016/j.procir.2017.02.041.
  • Wireman, T. 2014. Benchmarking Best Practices for Maintenance and Reliability. 3rd ed. South Norwalk, CT, USA: Industrial Press.
  • Wöstmann, R., P. Strauss, and J. Deuse. 2017. “Predictive Maintenance in der Produktion: Anwendungsfälle und Einführungsvoraussetzungen zur Erschließung ungenutzter Potentiale (Predictive Maintenance in production).” Wt-Online 7/8: 524–529.
  • Yan, J., Y. Meng, L. Lu, and L. Li. 2017. “Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance.” IEEE Access 5: 23484–23491. doi:10.1109/ACCESS.2017.2765544.
  • Zandieh, M., A. R. Khatami, and S. H. A. Rahmati. 2017. “Flexible Job Shop Scheduling under Condition-Based Maintenance: Improved Version of Imperialist Competitive Algorithm.” Applied Soft Computing 58: 449–464. doi:10.1016/j.asoc.2017.04.060.
  • Zarte, M., U. Wunder, and A. Pechmann, Concept and First Case Study for a Generic Predictive Maintenance Simulation in AnyLogic™, In Industrial Electronics Society, IECON 2017-43rd Annual Conference of the IEEE, Beijing, China, 2017. 3372–3377.
  • Zhang, Y., S. Ren, Y. Liu, and S. Si. 2017. “A Big Data Analytics Architecture for Cleaner Manufacturing and Maintenance Processes of Complex Products.” Journal of Cleaner Production 142: 626–641. doi:10.1016/j.jclepro.2016.07.123.
  • Zühlke, D. 2008. SmartFactory – A Vision Becomes Reality. In: Proceedings of the 17thWorld Congress IFAC’08, Seoul, Korea, 14101–14108.