Prediction Reliability of a Statistical Methodology for Gas Turbine Prognostics

Abstract

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%

Application of a Statistical Methodology for Gas Turbine Degradation Prognostics to Alstom Field Data

“…[11] and whose reliability was thoroughly investigated in Ref. [36], is the availability of a given number n T of trends over time of the considered parameter. The minimum number of required past trends (in this context, operation periods between two off-line washes are considered to be a trend) is three, as demonstrated in Ref.…”

“…[11,36], the prognostic methodology applied in this paper estimates system performance by evaluating the time change of a "state indicator" variable. For instance, the "state indicator" variable can be "1" when the component is in the operational state (i.e., higher-than-threshold operation) and "0" when it is in failed state (i.e., lower-than-threshold operation).…”

“…For the simulations carried out in this paper, according to Refs. [11,36], the model internal parameters (i.e., number of samples to calculate the sampling averages n sa and number of simulated histories through the SCE procedure (n h ) SCE ) are assumed equal to 10,000 and 100,000, respectively. Moreover, as discussed in Ref.…”

“…[11] and tested in Ref. [36] can be applied to both the health indices of gas turbine performance and to normalized measurable quantities. In fact, it requires the use of statistical tools and operation data, while it does not require a physics-based model of the considered system.…”

“…For details, the reader is addressed to [11,36]. (2) description of field data and discussion of the procedure used for data processing before feeding the prognostic methodology.…”