Component Map Tuning Procedure Using Adaptive Modeling

2012 ◽  
Author(s):  
Michel L. Verbist ◽  
Wilfried P. J. Visser ◽  
Rene Pecnik ◽  
Jos P. van Buijtenen
Keyword(s):  
Path Analysis ◽  
Gas Turbine ◽  
Engine Performance ◽  
Diagnostic Techniques ◽  
Reference Points ◽  
Performance Data ◽  
Adaptive Modeling ◽  
Real Component ◽  
Modeling Errors ◽  
Component Map

Performance models are effective tools for analysis of engine condition throughout the life cycle of a gas turbine engine. Component maps necessary for accurate performance modeling are typically not provided by the original equipment manufacturers. To compensate for the missing information, available maps of similar components are scaled to match component performance at one or more reference points. Although scaled maps can provide sufficiently accurate results close to the reference points, modeling errors tend to increase further away from these reference points. For applications such as gas path analysis, the resulting modeling errors can be of the same order of magnitude as the deterioration to be detected. This severely limits the application of such techniques. This article presents a component map tuning procedure that tunes maps with more detail than just scaling. The tuned maps are a closer match to real component performance. The tuning procedure combines the adaptive modeling capability of the Gas turbine Simulation Program (GSP) and on-wing measured engine performance data. On-wing measured engine performance data allows map tuning over a wider range of power settings compared to engine performance data measured in a test cell. Effects of measurement uncertainty and scatter, and effects of compressor bleed flows on the map tuning procedure are analyzed and discussed. The tuned component maps enabled more accurate component condition estimations, mainly characterized by less scatter. By improving the accuracy of gas path analysis with on-wing measured performance data, this work has enabled more effective use of performance diagnostic techniques in the aero-engine maintenance industry.

Experience With GSP as a Gas Path Analysis Tool

2006 ◽  
Author(s):  
W. P. J. Visser ◽  
H. Pieters ◽  
M. Oostveen ◽  
E. van Dorp
Keyword(s):  
Path Analysis ◽  
Gas Turbine ◽  
Reference Model ◽  
Measurement Data ◽  
Engine Performance ◽  
Analysis Data ◽  
Calibration Method ◽  
Analysis Tool ◽  
Operational Environment ◽  
Adaptive Modeling

SKF’s primary tool for gas turbine engine performance analysis is GSP (Gas turbine Simulation Program), a component based modeling environment that is developed at National Aerospace Laboratory NLR and Delft University of Technology, The Netherlands. One of the applications is gas path analysis (GPA) using GSP’s generic adaptive modeling capability. With GSP, gas path analysis has been applied to different aero engines at several maintenance facilities. Additional functionalities have been developed to analyze multiple engine operating points and combine results of different adaptive modeling configurations automatically, resulting in more accurate and reliable GPA results. A ‘multi-point calibration’ method for the reference model was developed providing a significant improvement of GPA accuracy and stability. Also, a method was developed using ‘multiple analysis cycles’ on different condition indicator subsets, which successfully generated values for all condition parameters in cases with fewer measurement parameters than condition indicators and where measurement data are unreliable. The method has been successfully demonstrated on the GEM42 turbo shaft engine. A number of case studies have shown GPA results corresponding to available maintenance notes and inspection data. The extension of the GSP GPA tool with a database system provides a useful tool for analyzing engine history and comparison of analyzed component conditions throughout the fleet. When a large amount of analysis data is stored in the database, statistic analyses, trending and data mining can be performed. Also maintenance work scope effect on engine performance can be predicted. In this paper, the newly developed GSP gas path analysis functionalities are described and experiences and results with the GEM42 engine operational environment are presented.

Gas Path Analysis on KLM In-Flight Engine Data

2011 ◽  
Author(s):  
Michel L. Verbist ◽  
Wilfried P. J. Visser ◽  
Jos P. van Buijtenen ◽  
Rob Duivis
Keyword(s):  
Path Analysis ◽  
Gas Turbine ◽  
Diagnostic Techniques ◽  
Operating Conditions ◽  
Performance Data ◽  
Analysis Tool ◽  
Incorrect Diagnosis ◽  
Wing Performance ◽  
Cell Data ◽  
Acceptance Tests

Gas-path-analysis (GPA) based diagnostic techniques enable health estimation of individual gas turbine components without the need for engine disassembly. Currently, the Gas turbine Simulation Program (GSP) gas path analysis tool is used at KLM Engine Services to assess component conditions of the CF6-50, CF6-80 and CFM56-7B engine families during post-overhaul performance acceptance tests. The engine condition can be much more closely followed if on-wing (i.e., in-flight) performance data are analyzed also. By reducing unnecessary maintenance due to incorrect diagnosis, maintenance costs can be reduced, safety improved and engine availability increased. Gas path analysis of on-wing performance data is different in comparison to gas path analysis with test cell data. Generally fewer performance parameters are recorded on-wing and the available data are more affected by measurement uncertainty including sensor noise, sensor bias and varying operating conditions. Consequently, this reduces the potential and validity of the diagnostic results. In collaboration with KLM Engine Services, the feasibility of gas path analysis with on-wing performance data is assessed. In this paper the results of the feasibility study are presented, together with some applications and case studies of preliminary GPA results with on-wing data.

Component Map Generation of a Gas Turbine Using Genetic Algorithms

2004 ◽  
Author(s):  
Changduk Kong ◽  
Seonghee Kho ◽  
Jayoung Ki
Keyword(s):  
Genetic Algorithms ◽  
Performance Analysis ◽  
Gas Turbine ◽  
Pressure Ratio ◽  
Engine Performance ◽  
Experimental Tests ◽  
Real Component ◽  
Map Generation ◽  
Turboshaft Engine ◽  
Component Map

In order to estimate the precise performance of the existing gas turbine engine, the component maps with more realistic performance characteristics are needed. Because the components maps are engine manufacturer’s propriety obtained from very expensive experimental tests, they are not provided to the customers, generally. Therefore, because the engineers, who are working the performance simulation, have been mostly relying on component maps scaled from the similar existing maps, the accuracy of the performance analysis using the scaled maps may be relatively lower than that using the real component maps. Therefore, a component map generation method using experimental data and the genetic algorithms are newly proposed in this study. The engine test unit to be used for map generation has a free power turbine type small turboshaft engine. In order to generate the performance map for components of this engine, after obtaining engine performance data through many experimental tests, and then the third order equations, which have relationships the mass flow function, the pressure ratio and the isentropic efficiency as to the engine rotational speed were derived by using the genetic algorithm. A steady-state performance analysis was performed with the generated maps of the compressor by the commercial gas turbine performance analysis program GASTURB (Kruzke, 2001). In order to verify predominance of the proposed scheme, the performance analysis results using the maps obtained by this study were compared with those using the compressor map provided by the engine manufacturer and the scaled turbine maps obtained from the GASTURB, as well as experimental results. In comparison, it was found that the component maps can be generated from the experimental test data by using the genetic algorithms, and it was confirmed that the analysis results using the generated maps were very similar to those using the scaled maps from the GASTURB.

Component Map Generation of a Gas Turbine Using Genetic Algorithms

2004 ◽  
Vol 128 (1) ◽  
pp. 92-96 ◽  
Author(s):  
Changduk Kong ◽  
Seonghee Kho ◽  
Jayoung Ki
Keyword(s):  
Experimental Data ◽  
Genetic Algorithms ◽  
Performance Analysis ◽  
Gas Turbine ◽  
Pressure Ratio ◽  
Engine Performance ◽  
Experimental Tests ◽  
Real Component ◽  
Map Generation ◽  
Component Map

In order to estimate the precise performance of the existing gas turbine engine, the component maps with more realistic performance characteristics are needed. Because the component maps are the engine manufacturer’s propriety obtained from very expensive experimental tests, they are not provided to the customers, generally. Therefore, because the engineers, who are working the performance simulation, have been mostly relying on component maps scaled from the similar existing maps, the accuracy of the performance analysis using the scaled maps may be relatively lower than that using the real component maps. Therefore, a component map generation method using experimental data and the genetic algorithms are newly proposed in this study. The engine test unit to be used for map generation has a free power turbine type small turboshaft engine. In order to generate the performance map for compressor of this engine, after obtaining engine performance data through experimental tests, and then the third order equations, which have relationships with the mass flow function, the pressure ratio, and the isentropic efficiency as to the engine rotational speed, were derived by using the genetic algorithms. A steady-state performance analysis was performed with the generated maps of the compressor by the commercial gas turbine performance analysis program GASTURB (Kurzke, 2001). In order to verify the proposed scheme, the experimental data for verification were compared with performance analysis results using traditional scaled component maps and performance analysis results using a generated compressor map by genetic algorithms (GAs). In comparison, it was found that the analysis results using the generated map by GAs were well agreed with experimental data. Therefore, it was confirmed that the component maps can be generated from the experimental data by using GAs and it may be considered that the more realistic component maps can be obtained if more various conditions and accurate sensors would be used.

Components Map Generation of Gas Turbine Engine Using Genetic Algorithms and Engine Performance Deck Data

2006 ◽  
Vol 129 (2) ◽  
pp. 312-317 ◽  
Author(s):  
Changduk Kong ◽  
Jayoung Ki
Keyword(s):  
Experimental Data ◽  
Genetic Algorithms ◽  
Gas Turbine ◽  
Test Data ◽  
Engine Performance ◽  
Scaling Method ◽  
Turbine Engine ◽  
Scaling Methods ◽  
Map Generation ◽  
Component Map

In order to estimate the gas turbine engine performance precisely, the component maps containing their own performance characteristics should be used. Because the components map is an engine manufacturer’s propriety obtained from many experimental tests with high cost, they are not provided to the customer generally. Some scaling methods for gas turbine component maps using experimental data or data partially given by engine manufacturers had been proposed in a previous study. Among them the map generation method using experimental data and genetic algorithms had showed the possibility of composing the component maps from some random test data. However not only does this method need more experimental data to obtain more realistic component maps but it also requires some more calculation time to treat the additional random test data by the component map generation program. Moreover some unnecessary test data may introduced to generate inaccuracy in component maps. The map generation method called the system identification method using partially given data from the engine manufacturer (Kong and Ki, 2003, ASME J. Eng. Gas Turbines Power, 125, 958–979) can improve the traditional scaling methods by multiplying the scaling factors at design point to off-design point data of the original performance maps, but some reference map data at off-design points should be needed. In this study a component map generation method, which may identify the component map conversely from some calculation results of a performance deck provided by the engine manufacturer using the genetic algorithms, was newly proposed to overcome the previous difficulties. As a demonstration example for this study, the PW206C turbo shaft engine for the tilt rotor type smart unmanned aerial vehicle which has been developed by Korea Aerospace Research Institute was used. In order to verify the proposed method, steady-state performance analysis results using the newly generated component maps were compared with them performed by the Estimated Engine Performance Program deck provided by the engine manufacturer. The performance results using the identified maps were also compared with them using the traditional scaling method. In this investigation, it was found that the newly proposed map generation method would be more effective than the traditional scaling method and the methods explained above.

Integration of Technology Capability for Performance Diagnostics of MS7001EA Using PYTHIA

2019 ◽  
Author(s):  
Dieni Indarti ◽  
Emmanuel O. Osigwe ◽  
Yi-Guang Li ◽  
Dody Widyantoro
Keyword(s):  
Path Analysis ◽  
Gas Turbine ◽  
Diagnostic Tool ◽  
Computer Software ◽  
Engine Performance ◽  
Performance Model ◽  
Diagnostic Purpose ◽  
Engine Operation ◽  
The Real ◽  
Operation Period

Abstract Gas turbine components are susceptible to degradation during operations; hence, the identification of the engine condition is really important for the gas turbine users. To this end, a comprehensive adaptive diagnostic tool is an important step to monitoring the engine health condition and planning appropriate maintenance actions, thereby increasing the availability and reliability of the unit, and at the same time reducing the operation and maintenance expenses. In this paper, the capability of PYTHIA; a computer software technology for engine diagnostic purpose using a non-linear gas path analysis was explored on GE MS7001EA industrial heavy duty gas turbine during a plot period of 12,000 hours. The method used in this paper was to adapt an accurate engine performance model from the real engine historical data readings, and by implicating multiple component degradation parameters onto the diagnostic tool; which represents the possible phenomena in the real engine operation period. The adaptive gas path analysis was used to identify the level of degradation or health indices of the gas turbine at the module level and its degraded performance compared with the actual engine data trending. The results obtained indicated the capability of PYTHIA to successfully adapt real engine data and detect fault patterns in response to implanted faults of selected measurement set during engine operation period. The deviations between the predicted and measured values showed a satisfactory result with a root mean square error (RMS) ≤ 0.004 and Gas Path Analysis index value ≥ 0.996. The component parameter degradation during the 12000 hours engine operation was detected, indicating a decrease in flow capacity by 2.1% for compressor and turbine by 2.8%.

Industrial Gas Turbine Health and Performance Assessment With Field Data

2016 ◽  
Vol 139 (5) ◽  
Author(s):  
I. Roumeliotis ◽  
N. Aretakis ◽  
A. Alexiou
Keyword(s):  
Performance Assessment ◽  
Gas Turbine ◽  
Engine Performance ◽  
Economic Assessment ◽  
Adaptive Modeling ◽  
Part Load ◽  
Cycle Simulation ◽  
Automated Method ◽  
On Line ◽  
And Performance

The paper presents a thorough analysis of the historical data and results acquired over a period of two years through an on-line real-time monitoring system installed at a combined heat and power (CHP) plant. For gas turbine health and performance assessment, a gas path analysis tool based on the adaptive modeling method is integrated into the system. An engine adapted model built through a semi-automated method is part of a procedure which includes a steam/water cycle simulation module and an economic module used for power plant performance and economic assessment. The adaptive modeling diagnostic method allowed for accurate health assessment during base and part load operation identifying and quantifying compressor recoverable deterioration and the root cause of an engine performance shift. Next, the performance and economic assessment procedure was applied for quantifying the economic benefit accrued by implementing daily on-line washing and for evaluating the financial gains if the off-line washings time intervals are optimized based on actual engine performance deterioration rates. The results demonstrate that this approach allows continuous health and performance monitoring at full and part load operation enhancing decision making capabilities and adding to the information that can be acquired through traditional analysis methods based on heat balance and base load correction curves.

scholarly journals Infrared Thermal Imaging System as a Diagnostic Tool for Gas Turbine Engine Faults

1994 ◽  
Author(s):  
J. D. MacLeod ◽  
P. Steckhan ◽  
D. He
Keyword(s):  
Gas Turbine ◽  
Infrared Thermography ◽  
Imaging System ◽  
National Research Council ◽  
Engine Performance ◽  
Diagnostic Techniques ◽  
Infrared Light ◽  
Infrared Thermal Imaging ◽  
Radiation Spectra ◽  
Project Objectives

With the cost of maintaining a fleet of gas turbine engines continuing to rise, there is a greater need to develop methods to diagnose engine deterioration and identify faulty engine components quickly and efficiently. The Structures, Materials and Propulsion Laboratory of the National Research Council of Canada (NRC) has established a program to develop and evaluate various diagnostic techniques. The effort is aimed at investigating the effects of typical in-service faults on engine performance characteristics. An important aspect of the engine test program is the evaluation of non-intrusive sensors to accurately measure gas turbine performance. Using infrared thermography, the measurement of temperature is accomplished non-intrusively using the infrared radiation spectra. This instrumentation provides an indirect measurement of temperature and does not interfere with the flow field being measured. The temperature patterns can be used to determine engine health, and identify possible fault conditions within the hot section of the engine. This paper describes the project objectives, the experimental installation, and the results of the performance evaluations. A description of the infrared thermography system, and the data reduction and analysis systems used to convert infrared light into temperature profile contours is given.

scholarly journals Discrimination of Rapid and Gradual Deterioration for an Enhanced Gas Turbine Life-cycle Monitoring and Diagnostics

2021 ◽  
Vol 12 (3) ◽  
Author(s):  
Amare Fentaye ◽  
Valentina Zaccaria ◽  
Konstantinos Kyprianidis
Keyword(s):  
Life Cycle ◽  
Path Analysis ◽  
Gas Turbine ◽  
Diagnostic System ◽  
Engine Performance ◽  
Diagnostic Systems ◽  
Integrated Method ◽  
Performance Trends ◽  
Cycle Monitoring ◽  
Long Term Impact

Advanced engine health monitoring and diagnostic systems greatly benefit users helping them avoid potentially expensive and time-consuming repairs by proactively identifying shifts in engine performance trends and proposing optimal maintenance decisions. Engine health deterioration can manifest itself in terms of rapid and gradual performance deviations. The former is due to a fault event that results in a short-term performance shift and is usually concentrated in a single component. Whereas the latter implies a gradual performance loss that develops slowly and simultaneously in all engine components over their lifetime due to wear and tear. An effective engine life-cycle monitoring and diagnostic system is therefore required to be capable of discriminating these two deterioration mechanisms followed by isolating and identifying the rapid fault accurately. In the proposed solution, this diagnostic problem is addressed through a combination of adaptive gas path analysis and artificial neural networks. The gas path analysis is applied to predict performance trends in the form of isentropic efficiency and flow capacity residuals that provide preliminary information about the deterioration type. Sets of neural network modules are trained to filter out noise in the measurements, discriminate rapid and gradual faults, and identify the nature of the root cause, in an integrated manner with the gas path analysis. The performance of the proposed integrated method has been demonstrated and validated based on performance data obtained from a three-shaft turbofan engine. The improvement achieved by the combined approach over the gas path analysis technique alone would strengthen the relevance and long-term impact of our proposed method in the gas turbine industry.

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