The Impact of Measurement Noise in GPA Diagnostic Analysis of a Gas Turbine Engine

Ntantis, Efstratios L.; Li, Yi-Guang

doi:10.1515/tjj-2013-0024

Cited by 5 publications

(3 citation statements)

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“…Gas path analysis (GPA) based on performance models of the engine is still one of the most common techniques. Various non-linear and adaptive models [14,15], also in combination with machine learning methods [16,17], have been proposed to overcome the typical shortcomings of GPA, such as smearing effect and high sensitivity to measurement noise [18,19]. Many efforts over the years have been made on identifying optimal sets of measurements and optimal combinations of measured values and performance factors to achieve desired diagnostics capability [9,20].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Model for Aero-Engines Fleet Condition Monitoring

et al. 2020

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Since aeronautic transportation is responsible for a rising share of polluting emissions, it is of primary importance to minimize the fuel consumption any time during operations. From this perspective, continuous monitoring of engine performance is essential to implement proper corrective actions and avoid excessive fuel consumption due to engine deterioration. This requires, however, automated systems for diagnostics and decision support, which should be able to handle large amounts of data and ensure reliability in all the multiple conditions the engines of a fleet can be found in. In particular, the proposed solution should be robust to engine-to-engine deviations and different sensors availability scenarios. In this paper, a probabilistic Bayesian network for fault detection and identification is applied to a fleet of engines, simulated by an adaptive performance model. The combination of the performance model and the Bayesian network is also studied and compared to the probabilistic model only. The benefit in the suggested hybrid approach is identified as up to 50% higher accuracy. Sensors unavailability due to manufacturing constraints or sensor faults reduce the accuracy of the physics-based method, whereas the Bayesian model is less affected.

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Section: Introductionmentioning

confidence: 99%

Probabilistic Model for Aero-Engines Fleet Condition Monitoring

et al. 2020

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“…The gas turbine diagnostic program used for this study is the GPA technique developed at Cranfield University [5]. The GPA technique has been applied to a gas turbine model engine, a civil high by-pass ratio turbofan engine, similar to Trent 500 manufactured by Rolls Royce plc [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Gas Turbine Component Leakage Fault on GPA Performance Diagnostics

Ntantis¹,

Botsaris²

2016

JESTR

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The leakage analysis is a key factor in determining energy loss from a gas turbine. Once the components assembly fails, air leakage through the opening increases resulting in a performance loss. Therefore, the performance efficiency of the engine cannot be reliably determined, without good estimates and analysis of leakage faults. Consequently, the implementation of a leakage fault within a gas turbine engine model is necessary for any performance diagnostic technique that can expand its diagnostics capabilities for more accurate predictions. This paper explores the impact of gas turbine component leakage fault on GPA (Gas Path Analysis) Performance Diagnostics. The analysis is demonstrated with a test case where gas turbine performance simulation and diagnostics code TURBOMATCH is used to build a performance model of a model engine similar to Rolls-Royce Trent 500 turbofan engine, and carry out the diagnostic analysis with the presence of different component fault cases. Conclusively, to improve the reliability of the diagnostic results, a leakage fault analysis of the implemented faults is made. The diagnostic tool used to deal with the analysis of the gas turbine component implemented faults is a model-based method utilizing a non-linear GPA.

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“…The main gas path components, namely compressor and turbine, are inherently reliable but the operation of the aero engines under hostile environments, such as varying conditions of load, temperature and speed, and the cycle sensitivity to component degradation, results into engine breakdowns and performance deterioration [1][2][3][4][5][6]. The effect of component degradation is that the efficiencies and capacities of these get changed, and in order to determine the degradation level, it is required to estimate the level of changes in efficiency and capacity [1,7,8]. Performance deterioration is inevitable; increases engine maintenance cost and ultimately affects the safety of the engine, the aircraft and the crew.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Methods for an Aircraft Engine Performance

Ntantis¹,

Botsaris²

2015

JESTR

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The main gas path components, namely compressor and turbine, are inherently reliable but the operation of the aero engines under hostile environments, results into engine breakdowns and performance deterioration. Performance deterioration increases the operating cost, due to the reduction in thrust output and higher fuel consumption, and also increases the engine maintenance cost. In times when economic considerations dominate airline operators' strategies, carrying out unnecessary rectification, can be very costly and time consuming. In an attempt to minimize such unexpected circumstances, having detailed knowledge prior to any inspection will allow the gas turbine user to take some of the maintenance action when it is necessary. Advanced engine-fault diagnostics tools offer the possibility of identifying degradation at the module level, determining the trends of these degradations during the usage of the engine, and planning the maintenance action ahead.

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The Impact of Measurement Noise in GPA Diagnostic Analysis of a Gas Turbine Engine

Cited by 5 publications

References 0 publications

Probabilistic Model for Aero-Engines Fleet Condition Monitoring

Probabilistic Model for Aero-Engines Fleet Condition Monitoring

The Impact of Gas Turbine Component Leakage Fault on GPA Performance Diagnostics

Diagnostic Methods for an Aircraft Engine Performance

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