Model-based Approach to Automated Calculation of Key Performance Indicators for Industrial Turbines
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
In recent years, the service business of the global turbo- machinery industry has undergone important changes. Many of these changes have been motivated by an increased demand for dedicated and systematic approaches to process safety, reliability, asset integrity and the overall health of the system. This has strengthened the role of key performance indicators (KPIs) as a means of providing guidance for the system’s health state and improve risk management. In order to provide trustable and accurate calculations of these performance indicators in an automated fashion, we argue for a model-based solution that deals with the complexity of diverse configurations and interdependences between system components. This paper presents a solution for calculating KPIs by a semi-automated process based on post-data processing from the site and specific system models. The models consist of a combination of system descriptions in terms of ontologies and complex event processing models. By virtue of our models, state indicator rules for KPI calculations can be formulated at different levels, identifying performance gaps and indicating precisely where action should be taken by the service engineers. With the adopted solution, we discuss the practical implementation and present results of our success story at Siemens AG for the Industrial Gas Turbines.
How to Cite
##plugins.themes.bootstrap3.article.details##
complex systems, gas turbines, model-based methods, performance analysis, complex event processing, ontology, key performance indicators
Ding, S. X., Yin, S., Peng, K., Hao, H., & Shen, B. (2013). A novel scheme for key performance indicator prediction and diagnosis with application to an industrial hot strip mill. Industrial Informatics, IEEE Transactions on, 9(4), 2239-2247.
Odgaard, P. F., Stoustrup, J., & Kinnaert, M. (2013). Fault- tolerant control of wind turbines: A benchmark model. Control Systems Technology, IEEE Transactions on, 21(4), 1168-1182.
Márquez, F. P. G., Tobias, A. M., Pérez, J. M. P., & Papaelias, M. (2012). Condition monitoring of wind turbines: Techniques and methods. Renewable Energy, 46, 169-178.
Forsthoffer, W. E. (2011). Forsthoffer's Best Practice Handbook for Rotating Machinery. Elsevier.
Ceschini, G. F., & Carlevaro, F. (2002, January). Gas turbine maintenance policy: a statistical methodology to prove interdependency between number of starts and running hours. In ASME Turbo Expo 2002: Power for Land, Sea, and Air (pp. 1137-1142). American Society of Mechanical Engineers.
IEEE Standard Definitions for Use in Reporting Electric Generating Unit Reliability, Availability, and Productivity. IEEE Std 762TM-2006. IEEE Power Engineering Soc.
Gas turbines - Procurement - Part 9: Reliability, availability, maintainability and safety. BS ISO 3977-9:1999. British Standards.
Baader, F, & Calvanese, D., & McGuinness, D., &. Nardi, D., & Patel-Schneider, P., (2003) The Description Logic Handbook. Cambridge University Press.
Chandrasekaran, B., Josephson, J. R., & Benjamins, V. R. (1999). What are ontologies, and why do we need them?. IEEE Intelligent systems, 14(1), 20-26.
Ming, D. Z. T. S. Z., & Jie, Y. D. C. (2002). Overview of Ontology. Acta Scicentiarum Naturalum Universitis Pekinesis, 38(9), 728-730.
Robins, D. (2010, February). Complex event processing. In Second International Workshop on Education Technology and Computer Science. Wuhan.
Wasserkrug, S., Gal, A., Etzion, O., & Turchin, Y. (2008, July). Complex event processing over uncertain data. In Proceedings of the second international conference on Distributed event-based systems (pp.253-264) ACM.
Luckham, D. (2002). The power of events (Vol. 204).Reading: Addison-Wesley.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.