The performance of gas turbines degrades over time and, as a consequence, a decrease in gas turbine performance parameters also occurs, so that they may fall below a given threshold value. Therefore, corrective maintenance actions are required to bring the system back to an acceptable operating condition. In today’s competitive market, the prognosis of the time evolution of system performance is also recommended, in such a manner as to take appropriate action before any serious malfunctioning has occurred and, as a consequence, to improve system reliability and availability. Successful prognostics should be as accurate as possible, because false alarms cause unnecessary maintenance and nonprofitable stops. For these reasons, a prognostic methodology, developed by the authors, is applied in this paper to assess its prediction reliability for several degradation scenarios typical of gas turbine performance deterioration. The methodology makes use of the Monte Carlo statistical method to provide, on the basis of the recordings of past behavior, a prediction of future availability, i.e., the probability that the considered machine or component can be found in the operational state at a given time in the future. The analyses carried out in this paper aim to assess the influence of the degradation scenario on methodology prediction reliability, as a function of a user-defined threshold and minimum value allowed for the parameter under consideration. A technique is also presented and discussed, in order to improve methodology prediction reliability by means a correction factor applied to the time points used for methodology calibration. The results presented in this paper show that, for all the considered degradation scenarios, the prediction error is lower than 4% (in most cases, it is even lower than 2%), if the availability is estimated for the next trend, while it is not higher than 12%, if the availability is estimated five trends ahead. The application of a proper correction factor allows the prediction errors after five trends to be reduced to approximately 5%.
Skip Nav Destination
Article navigation
October 2012
Gas Turbines: Controls, Diagnostics, And Instrumentation
Prediction Reliability of a Statistical Methodology for Gas Turbine Prognostics
Mauro Venturini,
Mauro Venturini
Dipartimento di Ingegneria, Università degli Studi di Ferrara, Via G. Saragat 1-44122 Ferrara
, Italy
Search for other works by this author on:
Nicola Puggina
Nicola Puggina
San Marco Bioenergie SpA, Via Brera 16, 20121, Milano,
Italy
Search for other works by this author on:
Mauro Venturini
Dipartimento di Ingegneria, Università degli Studi di Ferrara, Via G. Saragat 1-44122 Ferrara
, Italy
Nicola Puggina
San Marco Bioenergie SpA, Via Brera 16, 20121, Milano,
Italy
J. Eng. Gas Turbines Power. Oct 2012, 134(10): 101601 (9 pages)
Published Online: August 22, 2012
Article history
Received:
June 21, 2012
Revised:
June 22, 2012
Online:
August 22, 2012
Published:
August 22, 2012
Citation
Venturini, M., and Puggina, N. (August 22, 2012). "Prediction Reliability of a Statistical Methodology for Gas Turbine Prognostics." ASME. J. Eng. Gas Turbines Power. October 2012; 134(10): 101601. https://doi.org/10.1115/1.4007064
Download citation file:
Get Email Alerts
Experimental Characterization of Superheated Ammonia Spray from a Single-hole ECN Spray M Injector
J. Eng. Gas Turbines Power
Improving the Predictive Capability of Empirical Heat Transfer Correlations for Hydrogen Internal Combustion Engines
J. Eng. Gas Turbines Power (October 2025)
The Hybrid Pathway to Flexible Power Turbines: Part IV, Automated Construction of Mesh Derived Thermal Network Models for Fast Full-Machine Thermal Analysis
J. Eng. Gas Turbines Power (October 2025)
Related Articles
Development of a Statistical Methodology for Gas Turbine Prognostics
J. Eng. Gas Turbines Power (February,2012)
Optimal Failure-Finding Intervals for Heat Shields in a Gas Turbine Combustion Chamber Using a Multicriteria Approach
J. Eng. Gas Turbines Power (July,2015)
Application of a Statistical Methodology for Gas Turbine Degradation Prognostics to Alstom Field Data
J. Eng. Gas Turbines Power (September,2013)
Reliability of Wind Turbine Technology Through Time
J. Sol. Energy Eng (August,2008)
Related Proceedings Papers
Related Chapters
Reasons for Lay-Up
Consensus for the Lay-up of Boilers, Turbines, Turbine Condensors, and Auxiliary Equipment (CRTD-66)
Expert Systems in Condition Monitoring
Tribology of Mechanical Systems: A Guide to Present and Future Technologies
A Simplified Expert Elicitation Guideline (PSAM-0089)
Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM)