Gas Turbines: Controls, Diagnostics, and Instrumentation

Prediction Reliability of a Statistical Methodology for Gas Turbine Prognostics

[+] Author and Article Information
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 134(10), 101601 (Aug 22, 2012) (9 pages) doi:10.1115/1.4007064 History: Received June 21, 2012; Revised June 22, 2012; Published August 22, 2012; Online August 22, 2012

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%.

Copyright © 2012 by American Society of Mechanical Engineers
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Figure 1

Prognostic methodology

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Figure 2

Sample of two Q(t) trends and significant time frames

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Figure 3

Prediction errors for scenario #1 (Qmin  = 0.980; ΔQ0  = 0.010)

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Figure 4

Prediction errors for scenario #2 (Qmin  = 0.980; ΔQ0  = 0.005)

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Figure 5

Prediction errors for scenario #3 (Qmin  = 0.960; ΔQ0  = 0.010)

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Figure 6

Prediction errors for scenario #4 (Qmin  = 0.960; ΔQ0  = 0.005)

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Figure 7

Prediction errors for scenario #5 (Qmin  = 0.900; ΔQ0  = 0.010)

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Figure 8

Prediction errors for scenario #6 (Qmin  = 0.900; ΔQ0  = 0.005)

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Figure 9

Prediction errors for scenario #4, calculated by applying the correction factor Δtf - (a) high Qthr ; (b) medium Qthr ; (c) low Qthr

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Figure 10

RMSE3,8 prediction errors for scenario #1 through #4, calculated by applying the correction factor Δtf (medium Qthr )



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