Model-based Approach to Automated Calculation of Key Performance Indicators for Industrial Turbines



Published Oct 18, 2015
Gulnar Mehdi Davood Naderi Giuseppe Ceschini Alexey Fishkin Sebastian Brandt Stuart Watson Mikhail Roshchin


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

Mehdi, G. ., Naderi, D. ., Ceschini, G. ., Fishkin, A. ., Brandt, S., Watson, S. ., & Roshchin, M. . (2015). Model-based Approach to Automated Calculation of Key Performance Indicators for Industrial Turbines. Annual Conference of the PHM Society, 7(1).
Abstract 206 | PDF Downloads 210



complex systems, gas turbines, model-based methods, performance analysis, complex event processing, ontology, key performance indicators

Ceschini, G. F., & Saccardi, D. (2002). Availability centered maintenance (ACM), an integrated approach. InReliability and Maintainability Symposium, 2002. Proceedings. Annual (pp. 26-31). IEEE.

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.
Technical Research Papers