A Generalised Likelihood Ratio Test for Adaptive Gas Turbine Health Monitoring

Borguet, Sébastien; ́onard, O. Le

doi:10.1115/gt2008-50117

Cited by 11 publications

(7 citation statements)

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“…Otherwise, gradual deterioration, e.g., compressor fouling or turbine erosion, can be tracked and its time evolution can be predicted [16]. The reason for the interest towards gradual deterioration is due to the fact that (i) it is always experienced in field applications and, (ii) though it does not usually lead to catastrophic consequences, it may lead to unprofitable machine operation (e.g., due to ever increasing fuel costs) or, in the worst case, to damages that can be hardly recovered through subsequent maintenance.…”

Section: Introductionmentioning

confidence: 99%

Prediction Reliability of a Statistical Methodology for Gas Turbine Prognostics

Venturini

Puggina²

2012

Journal of Engineering for Gas Turbines and Power

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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%

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Section: Introductionmentioning

confidence: 99%

Prediction Reliability of a Statistical Methodology for Gas Turbine Prognostics

Venturini

Puggina²

2012

Journal of Engineering for Gas Turbines and Power

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“…In this study, since there was no real data available to characterize true noise levels, simplistic normal noise distributions were assumed based on information available from the literature [13,[16][17][18]. However, to make the signal noise non-trivial, mixture distributions were used and all of these noise sources were combined to present similar challenges in a realistic manner.…”

mentioning

confidence: 99%

Damage propagation modeling for aircraft engine run-to-failure simulation

Saxena

Goebel

Simon

et al. 2008

2008 International Conference on Prognostics and Health Management

1,110

676

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This paper describes how damage propagation can be modeled within the modules of aircraft gas turbine engines. To that end, response surfaces of all sensors are generated via a thermo-dynamical simulation model for the engine as a function of variations of flow and efficiency of the modules of interest. An exponential rate of change for flow and efficiency loss was imposed for each data set, starting at a randomly chosen initial deterioration set point. The rate of change of the flow and efficiency denotes an otherwise unspecified fault with increasingly worsening effect. The rates of change of the faults were constrained to an upper threshold but were otherwise chosen randomly. Damage propagation was allowed to continue until a failure criterion was reached. A health index was defined as the minimum of several superimposed operational margins at any given time instant and the failure criterion is reached when health index reaches zero. Output of the model was the time series (cycles) of sensed measurements typically available from aircraft gas turbine engines. The data generated were used as challenge data for the Prognostics and Health Management (PHM) data competition atPHM'08.

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“…The most common measures to reduce fouling are compressor washing and inlet filter exchange. Among the many papers produced on fouling effects, the following specifically deal with the time evolution of compressor fouling and its effects over time on gas turbine performance [19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

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

Venturini

Therkorn

2013

Journal of Engineering for Gas Turbines and Power

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In this paper, a prognostic methodology is applied to gas turbine field data to assess its capability as a predictive tool for degradation effects. On the basis of the recordings of past behavior, the methodology provides a prediction of future performance, i.e., the probability that degradation effects are at an acceptable level in future operations. The analyses carried out in this paper consider two different parameters (power output and compressor efficiency) of three different Alstom gas turbine power plants (gas turbine type GT13E2, GT24, and GT26). To apply the prognostic methodology, site specific degradation threshold values were defined, to identify the time periods with acceptable degradation (i.e., higher-than-threshold operation) and the time periods where maintenance activities are recommended (i.e., lower-than-threshold operation). This paper compares the actual distribution of the time points until the degradation limit is reached (discrete by nature) to the continuously varying distribution of the time points simulated by the probability density functions obtained through the prognostic methodology. Moreover, the reliability of the methodology prediction is assessed for all the available field data of the three gas turbines and for two values of the threshold. For this analysis, the prognostic methodology is applied by considering different numbers of degradation periods for methodology calibration and the accuracy of the next forecasted trends is compared to the real data. Finally, this paper compares the prognostic methodology prediction to a “purely deterministic” prediction chosen to be the average of the past time points of higher-than-threshold operations. The results show that, in almost all cases, the prognostic methodology allows a better prediction than the “purely deterministic” approach for both power and compressor efficiency degradation. Therefore, the prognostic methodology seems to be a robust and reliable tool to predict gas turbine power plant “probabilistic” degradation

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A Generalised Likelihood Ratio Test for Adaptive Gas Turbine Health Monitoring

Cited by 11 publications

References 0 publications

Prediction Reliability of a Statistical Methodology for Gas Turbine Prognostics

Prediction Reliability of a Statistical Methodology for Gas Turbine Prognostics

Damage propagation modeling for aircraft engine run-to-failure simulation

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

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