Abstract
Successful transition to Industry 4.0 requires cross domain and interdisciplinary research to develop new models for enhancing data and predictive analytics. Predictive models in particular should be applied to real time and remotely maintenance cost planning, monitoring and controlling of cyber physical production systems (CPPS). This paper presents a knowledge-based model, Costprove, discusses its mathematical meta-analysis approach for evidence extraction, and studies its application in the state-of-the-art industry towards its prospective in causality detection and predictive maintenance cost controlling of CPPS.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Literatur
Ansari, F., 2014. Meta-analysis of knowledge assets for continuous improvement of maintenance cost controlling. Dissertation, University of Siegen, Germany.
Ansari, F., Uhr, P. & Fathi, M., 2014. Textual Meta-analysis of Maintenance Management’s Knowledge Assets. International Journal of Services, Economics and Management. Inderscience Enterprises Ltd., pp. 14–37.
Biedermann, H., Ed., 2014. Instandhaltung im Wandel (Maintenace in Transition). TÜV Rheinland Group, Cologne, Germany.
Dienst, S., Ansari, F. & 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.
Hahn, D. & Laßman, G., 1993. Produktionswirtschaft-Controlling industrieller Produktion (Production Economy-Controlling Industrial Production). Heildelberg: Physica-Verlag, p. 353.
Niggemann, O. & Lohweg, V., 2015. On the Diagnosis of Cyber-Physical Production Systems: State-of-the-Art and Research Agenda. Austin, Texas, USA, Association for the Advancement of Artificial Intelligence.
Ruiz-Arenas, S., Horváth, I., Mejía-Gutiérrez, R. & Opiyo, E., 2014. Towards the Maintenance Principles of Cyber-Physical Systems. Journal of Mechanical Engineering, 60 (12), pp. 815–831.
Sharma, A.B., Ivancic, F., Niculescu-Mizil, A., Chen, H. & Jiang, G., 2014. Modeling and Analytics for Cyber-Physical Systems in the Age of Big Data. ACM SIGMETRICS Performance Evaluation Review, 41 (4), pp. 74-77.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ansari, F., Fathi, M. (2016). Meta-analysis of Maintenance Knowledge Assets Towards Predictive Cost Controlling of Cyber Physical Production Systems. In: Niggemann, O., Beyerer, J. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48838-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-662-48838-6_13
Published:
Publisher Name: Springer Vieweg, Berlin, Heidelberg
Print ISBN: 978-3-662-48836-2
Online ISBN: 978-3-662-48838-6
eBook Packages: EngineeringEngineering (R0)