A case study of predictive maintenance using data analysis for a flexible manufacturing line

Carmen Stan, Mihaela Oprea, Alexandru Calin Stan

Abstract


Abstract – In the last decades, the manufacturing industry has been in continuous development with technological breakthrough and enhancements. The purpose of enhancing production efficiency it is directly linked with the trend in costumers’ requirements, needs, product competition and availability on the market. This paper presents a predictive maintenance strategy based on data analytics for a flexible production line using the historical warning errors of the equipment of the line to achieve a condition-based maintenance plan.


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References


Cao Q., Zanni-Merk C., Samet A., Reich C., de Bertrand de Beuvron F., Beckmann A., Giannetti C. (2022). KSPMI: A knowledge-based system for predictive maintenance in Industrie 4.0, Robotics and Computer-Integrated Manufacturing, 74.

Dalota M.D. (1996). The enterprise of the future implementation strategy (Romanian), Published by Sedona, Timisoara.

Eversheim W. and Herrmann P. (1982). Recent trends in flexible automated manufacturing, Journal of Manufacturing Systems, Volume 1, Issue 2, pp. 139-148.

Grabot B. (2020). Rule mining in maintenance: Analyzing large knowledge bases, Computers & Industrial Engineering, 139.

Hartmann C., Opritescu D., Volk W., Schmiedl F., Ritter M., Gritzmann P. (2022). A knowledge-based automated driving approach for flexible production of individualized sheet metal parts, Knowledge-based Systems, 244.

Kang Z., Catal C., Tekinerdojan B. (2022). Product failure detection for production line using data-driving model, Expert Systems with Applications, 202.

Mehrabi M.G, Ulsoy A.G., Koren Y. and Heytler P. (2002) Trends and perspectives in flexible and reconfigurable manufacturing systems, Journal of Intelligent manufacturing, pp.135-146, April 2002.

Morosan A.D. (2013). Efficient software system for flexible manufacturing lines, in Romanian, PhD Thesis, University of Transilvania, Brasov.

Polenghi A., Roda I., Macchi M., Pozzetti A., Panetto H. (2022). Knowledge reuse for ontology modelling in maintenance and industrial asset management, Journal of Industrial Informatics Integration, 27.

Ramírez-Durán V. J., Berges I., Illarramendi A. (2020). ExtruOnt: An ontology for describing a type of manufacturing machine for Industrie 4.0 systems, Semantic Web, 11(6), pp. 887-909.

Roldán-Molina G. R., Ruano-Ordás D., Basto-Fernandes V., Méndez J. R. (2021). An ontology knowledge inspection methodology for quality assessment and continuous improvement, Data & Knowledge Engineering, 133.

Tao F. (2018). Data-driven smart manufacturing, Journal of Manufacturing Systems, 48, pp. 157-169.

Peter P., David Ž., Josef B. (2019). Historical Overview of Maintenance Management Strategies: Development from Breakdown Maintenance to Predictive Maintenance in Accordance with Four Industrial Revolutions”, Proceedings of the International Conference on Industrial Engineering and Operations Management Pilsen, Czech Republic, July 23-26.

https://www.reliableplant.com/Read/11785/overall-equipment-effectiveness

https://www.tibco.com/reference-center/what-is-overall-equipment-effectiveness


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