Research Papers: Gas Turbines: Oil and Gas Applications

Development of a Statistical Methodology for Gas Turbine Prognostics

[+] Author and Article Information
Nicola Puggina

Dipartimento di Ingegneria,  Università degli Studi di Ferrara, Via G. Saragat, 1, 44122 Ferrara, Italy

Mauro Venturini

Dipartimento di Ingegneria,  Università degli Studi di Ferrara, Via G. Saragat, 1, 44122 Ferrara, Italymauro.venturini@unife.it

J. Eng. Gas Turbines Power 134(2), 022401 (Dec 16, 2011) (9 pages) doi:10.1115/1.4004185 History: Received April 27, 2011; Revised April 27, 2011; Published December 16, 2011; Online December 16, 2011

To optimize both production and maintenance, from both a technical and an economical point of view, it would be advisable to predict the future health condition of a system and of its components, starting from field measurements taken in the past. For this purpose, this paper presents a methodology, based on the Monte Carlo statistical method, which aims to determine the future operating state of a gas turbine. The methodology allows the system future availability to be estimated, to support a prognostic process based on past historical data trends. One of the most innovative features is that the prognostic methodology can be applied to both global and local performance parameters, as, for instance, machine specific fuel consumption or local temperatures. First, the theoretical background for developing the prognostic methodology is outlined. Then, the procedure for implementing the methodology is developed and a simulation model is set up. Finally, different degradation-over-time scenarios for a gas turbine are simulated and a sensitivity analysis on methodology response is carried out, to assess the capability and the reliability of the prognostic methodology. The methodology proves robust and reliable, with a prediction error lower than 2%, for the availability associated with the next future data trend.

Copyright © 2012 by American Society of Mechanical Engineers
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Figure 1

Prognostic methodology

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Figure 2

Data trends for scenarios #2 (a) and #3 (b)

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Figure 3

Influence of the number of generated histories and of samples for MLE method (scenario #3, Qthr  = 0.980)

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Figure 4

Availability for scenario #3 (medium Qthr ), as a function of the number of available trends used for methodology calibration

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Figure 5

Availability for scenario #3 (high, medium, and low Qthr ), by using three or seven trends for methodology calibration

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Figure 6

Availability for scenarios #1, #2, #3 and #4 (medium Qthr ; 4 trends used for methodology calibration)

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Figure 7

Prediction errors for scenario #3 (medium Qthr )

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Figure 8

RMSE prediction errors for scenario #3 (high, medium, and low Qthr )




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