Multi-objective optimization of OEE (Overall Equipment Effectiveness) regarding production speed and energy consumption

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Published Jul 8, 2014
Adriaan Van Horenbeek Liliane Pintelon Abdellatif Bey-Temsamani Andrei Bartic

Abstract

Using condition monitoring to track machine health and trigger maintenance actions is a proven best practice. By monitoring machinery health, costly failures are avoided and downtime due to outages is reduced. This results in an improved OEE (Overall Equipment Effectiveness). Many papers discuss the implementation of condition monitoring to prevent failures and optimize maintenance interventions. However, much less attention is paid to the use of condition monitoring information in order to optimize production capacity of a machine or a plant. This optimization is often translated in production plants by maximizing the production capacity (speed) and minimizing machine’s downtime. As energy consumption is becoming more and more an important decision criterion in modern manufacturing plants, the former optimization needs to take this parameter into account. As such a trade-off has to be made between the gain in capacity and the cost of the additional energy consumed. Therefore, in this paper we will develop a multi-objective optimization of OEE to allow multiple-criteria decision making. More precisely, the goal of this paper is to establish the link between condition monitoring information and production capacity optimization by continuously adjusting production parameters (i.e. production speed) taking into account the machine’s condition and the energy consumption.

How to Cite

Horenbeek, A. V., Pintelon, L., Bey-Temsamani, A., & Bartic, A. (2014). Multi-objective optimization of OEE (Overall Equipment Effectiveness) regarding production speed and energy consumption. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1525
Abstract 258 | PDF Downloads 220

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Keywords

production optimization, overall equipment effectiveness (OEE), condition monitoring, maintenance, energy consumption, multi-objective

References
R. K. Mobley, (1990). An introduction to predictive maintenance, Van Nostrand Reinhold.
W. Sholom & I. Nitin, (1998). Predictive data mining: a practical guide. Morgan Kauffmann.
J. Blair & A. Shirkhodaie, (2001). Diagnosis and prognosis of bearings using data mining and numerical visualization techniques, Proceedings of the 33rd Southeastern Symposium on System Theory, pp. 395-399.
K. M. Goh, T. Tjahjono & Subramaniam, (2006). A review of research in manufacturing prognostics, IEEE International Conference on Industrial Informatics, pp.417-422.
A. Bey-Temsamani, M. Engels, A. Motten, S. Vandenplas & A. P. Ompussunggu, (2009). A practical approach to combine data mining and prognostics for improved predictive maintenance, Proceedings of 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Workshop Data Mining Case Studies, pp. 37-44.
A. Van Horenbeek, A. Bey-Temsamani, S. Vandenplas, L. Pintelon & B. De Ketelaere (2011). Prognostics for Optimal Maintenance: maintenance cost versus product quality optimization for industrial cases, Proceedings of the 6th World Congress on Engineering Asset Management.
A. Bey-Temsamani, A. Van Horenbeek, B. De Ketelaere, L. Pintelon, A. Bartic (2013). Prognostics for optimal maintenance: industrial production capacity optimization using temperature condition monitoring, Proceedings of the Condition Monitoring and Diagnostics Engineering Management (COMADEM).
Muchiri P, Pintelon L. Performance measurement using overall equipment effectiveness (OEE): literature review and practical application discussion. International Journal of Production Research. 2008;46:3517-35.
Nakajima. Introduction to TPM: Total Productive Maintenance: Productivity Press; 1988.
Section
Technical Papers