Abstract
The complexity and data-driven characteristics of Cyber Physical Production Systems (CPPS) impose new requirements on maintenance strategies and models. Maintenance in the era of Industry 4.0 should, therefore, advances prediction, adaptation and optimization capabilities in horizontally and vertically integrated CPPS environment. This paper contributes to the literature on knowledge-based maintenance by providing a new model of prescriptive maintenance, which should ultimately answer the two key questions of “what will happen, when? and “how should it happen?”, in addition to “what happened?” and “why did it happen?”. In this context, we intend to go beyond the scope of the research project “Maintenance 4.0” by i) proposing a data-model considering multimodalities and structural heterogeneities of maintenance records, and ii) providing a methodology for integrating the data-model with Dynamic Bayesian Network (DBN) for the purpose of learning cause-effect relations, predicting future events, and providing prescriptions for improving maintenance planning.
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References
1. Azadeh, A., Salehi, V., Jokar, M., and Asgari, A., 2016. An Integrated Multi-Criteria Computer Simulation-AHP-TOPSIS Approach for Optimum Maintenance Planning by Incorporating Operator Error and Learning Effects. Intelligent Industrial Systems, 2 (1), pp. 35-53.
2. Windmann, S., Eickmeyer, J., Jungbluth, F., Badinger, J., and Niggemann, O., 2016. Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases. In Machine Learning for Cyber Physical Systems. Springer, Berlin Heidelberg, pp. 45-50.
3. Li, P., and Niggemann, O., 2016. Improving Clustering based Anomaly Detection with Concave Hull: An Application in Fault Diagnosis of Wind Turbines. In 2016 IEEE 14th International Conference on Industrial Informatics (INDIN), IEEE Press, pp. 463-466.
4. Paschke, F., Bayer, C., and Enge-Rosenblatt, O., 2016. A Generic Approach for Detection of Wear-out Situations in Machine Subsystems. In 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE Press, pp. 1-4.
5. Ansari, F., Uhr, P., and Fathi, M., 2014. Textual Meta-analysis of Maintenance Management’s Knowledge Assets. International Journal of Services, Economics and Management, 6 (1), pp. 14-37.
6. Dienst, S., Ansari, F., and Fathi, M., 2015. Integrated System for Analyzing Maintenance Records in Product Improvement. The International Journal of Advanced Manufacturing Technology, 76 (1-4), Springer, pp. 545-564.
7. Ansari, F., and Fathi, M., 2016. Meta-analysis of Maintenance Knowledge Assets towards Predictive Cost Controlling of Cyber Physical Production Systems. In Machine Learning for Cyber Physical Systems. Springer, Berlin Heidelberg, pp. 103-110.
8. Glawar, R., Kemeny, Z., Nemeth, T., Matyas, K., Monostori, L., and Sihn, W., 2016. A Holistic Approach for Quality Oriented Maintenance Planning Supported by Data Mining Methods. Procedia CIRP, Vol. 57, pp. 259 - 264.
9. Flatt, H., Koch, N., Röcker, C., Günter, A., & Jasperneite, J., 2015. A Context-aware Assistance System for Maintenance Applications in Smart Factories based on Augmented Reality and Indoor Localization. In 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), IEEE Press, pp. 1-4.
10. Wang, J., Zhang, L., Duan, L., and Gao, R. X., 2017. A New Paradigm of Cloud-based Predictive Maintenance for Intelligent Manufacturing. Journal of Intelligent Manufacturing, 28 (5), pp. 1125-1137.
11. Abramovici, M., Gebus, P., Göbel, J. C., and Savarino, P., 2017. Provider-Driven Knowledge Allocation Concept for Improving Technical Repair Tasks in IPS 2 Networks. Procedia CIRP, Vol. 64, pp. 381-386.
12. Bärenfänger-Wojciechowski, S., Austerjost, M., and Henke, M., 2017. Smart Maintenance-Asset Management der Zukunft: Ein integrativer Management-Ansatz (Smart Maintenance - Asset Management for the Future: An Integrative Management Approach). wt-online 1/2- 2017, Springer-VDI Verlag, pp. 102-106.
13. Linstedt, D., and Olschimke, M., 2015. Building a Scalable Data Warehouse with Data Vault 2.0, 1st Edition, Elsevier, pp. 17-32 and 89-121.
14. Kelleher, J. D., Mac Namee, B., and D’Arcy, A., 2015. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies, MIT Press, pp. 292-306.
15. Gonzales, C., Dubuisson, S., and Manfredotti, C. E., 2015. A New Algorithm for Learning Non-Stationary Dynamic Bayesian Networks with Application to Event Detection. In FLAIRS Conference, pp. 564-569.
16. Robinson, J. W., and Hartemink, A. J. 2010. Learning Non-Stationary Dynamic Bayesian Networks. Journal of Machine Learning Research, 11, pp. 3647-3680.
17. Mihajlovic, V., and Petkovic, M., 2001. Dynamic Bayesian Networks: A State of the Art. Technical Report, University of Twente, The Netherlands.
Acknowledgement
The authors would like to acknowledge the financial support of the Austrian Research Promotion Agency (FFG) for funding the research project of “Maintenance 4.0” (2014-2017) under the grant number 843668.
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Ansari, F., Glawar, R., Sihn, W. (2020). Prescriptive Maintenance of CPPS by Integrating Multimodal Data with Dynamic Bayesian Networks. In: Beyerer, J., Maier, A., Niggemann, O. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation, vol 11. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-59084-3_1
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DOI: https://doi.org/10.1007/978-3-662-59084-3_1
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