Application of Gas Path Analysis to Compressor Diagnosis of an Industrial Gas Turbine Using Field Data

2014 ◽  
Author(s):  
J. Blinstrub ◽  
Y. G. Li ◽  
M. Newby ◽  
Q. Zhou ◽  
G. Stigant ◽  
...  
Keyword(s):  
Path Analysis ◽  
Gas Turbine ◽  
Field Data ◽  
Measurement Data ◽  
Diagnostic Technique ◽  
Operating Conditions ◽  
Maintenance Cost ◽  
Diagnostic Analysis ◽  
Major Life ◽  
Monitoring And Diagnostics

Maintenance cost is one of the major life cycle costs of gas turbine engines. To reduce the maintenance costs, the maintenance should be changed from preventive (or scheduled) maintenance to predictive (or condition-based) maintenance where condition monitoring and diagnostics become crucially important. This paper represents the application of a gas path diagnostic technique, Gas Path Analysis, to the diagnostic analysis of an aero-derivative gas turbine (GE LM2500+) operated by Manx Electricity Authority in the Isle of Man, UK. In the application, an engine thermodynamic model is created and adapted to the performance of the engine using field data obtained at different operating conditions. Different data pre-processing methods are presented and compared in the diagnostic analysis. The uncertainty of measurement data is analysed and the most suitable measurements are identified in the prediction of key gas path component degradation. A non-linear GPA diagnostic analysis provides promising results for the prediction of compressor degradation and the performance improvement due to a compressor water washing. Such diagnostic information would be very useful for maintenance engineers to optimise their maintenance activities including overhauls and compressor washing.

Computer Models for Education on Performance Monitoring and Diagnostics of Gas Turbines

2002 ◽  
Vol 30 (3) ◽  
pp. 204-218 ◽  
Author(s):  
K. Mathioudakis ◽  
A. Stamatis ◽  
A. Tsalavoutas ◽  
N. Aretakis
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Performance Monitoring ◽  
Measurement Data ◽  
Performance Model ◽  
Operating Conditions ◽  
Engine Operation ◽  
Monitoring And Diagnostics ◽  
Engine Components ◽  
The Way

The paper discusses how performance models can be incorporated in education on the subject of gas turbine performance monitoring and diagnostics. A particular performance model, built for educational purposes, is employed to demonstrate the different aspects of this process. The way of building a model is discussed, in order to ensure the connection between the physical principles used for diagnostics and the structure of the software. The first aspect discussed is model usage for understanding gas turbine behaviour under different operating conditions. Understanding this behaviour is essential, in order to have the possibility to distinguish between operation in ‘healthy’ and ‘faulty’ engine condition. A graphics interface is used to present information in different ways such as operating line, operating points on component maps, interrelation between performance variables and parameters. The way of studying faulty engine operation is then presented, featuring a novel comparison to existing simulation programs. Faults can be implanted into different engine components and their impact on engine performance studied. The notion of fault signatures on measured quantities is explained. The model has also a diagnostic capability, allowing the introduction of measurement data from faulty engines and providing a diagnosis, namely a picture of how the performance of engine components has deviated from a ‘healthy’ condition

Calibration and Uncertainty Quantification of Gas Turbine Performance Models

2015 ◽  
Author(s):  
Manuel Arias Chao ◽  
Darrel S. Lilley ◽  
Peter Mathé ◽  
Volker Schloßhauer
Keyword(s):  
Uncertainty Quantification ◽  
Gas Turbine ◽  
Bayesian Approach ◽  
Least Squares Method ◽  
Measurement Data ◽  
Operating Conditions ◽  
Performance Models ◽  
Regression Problem ◽  
Calibration Problem ◽  
Calibration Parameters

Calibration and uncertainty quantification for gas turbine (GT) performance models is a key activity for GT manufacturers. The adjustment between the numerical model and measured GT data is obtained with a calibration technique. Since both, the calibration parameters and the measurement data are uncertain the calibration process is intrinsically stochastic. Traditional approaches for calibration of a numerical GT model are deterministic. Therefore, quantification of the remaining uncertainty of the calibrated GT model is not clearly derived. However, there is the business need to provide the probability of the GT performance predictions at tested or untested conditions. Furthermore, a GT performance prediction might be required for a new GT model when no test data for this model are available yet. In this case, quantification of the uncertainty of the baseline GT, upon which the new development is based on, and propagation of the design uncertainty for the new GT is required for risk assessment and decision making reasons. By using as a benchmark a GT model, the calibration problem is discussed and several possible model calibration methodologies are presented. Uncertainty quantification based on both a conventional least squares method and a Bayesian approach will be presented and discussed. For the general nonlinear model a fully Bayesian approach is conducted, and the posterior of the calibration problem is computed based on a Markov Chain Monte Carlo simulation using a Metropolis-Hastings sampling scheme. When considering the calibration parameters dependent on operating conditions, a novel formulation of the GT calibration problem is presented in terms of a Gaussian process regression problem.

Detection and Classification of Sensor Anomalies in Gas Turbine Field Data

2018 ◽  
Author(s):  
Giuseppe Fabio Ceschini ◽  
Lucrezia Manservigi ◽  
Giovanni Bechini ◽  
Mauro Venturini
Keyword(s):  
Anomaly Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Field Data ◽  
Time Correlation ◽  
Detection Algorithm ◽  
Anomaly Classification ◽  
Monitoring And Diagnostics ◽  
Faulty Sensors

Anomaly detection and classification is a key challenge for gas turbine monitoring and diagnostics. To this purpose, a comprehensive approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (named DCIDS) was developed by the authors in previous papers. The methodology consists of an Anomaly Detection Algorithm (ADA) and an Anomaly Classification Algorithm (ACA). The ADA identifies anomalies according to three different levels of filtering. Anomalies are subsequently analyzed by the ACA to perform their classification, according to time correlation, magnitude and number of sensors in which an anomaly is contemporarily identified. The performance of the DCIDS approach is assessed in this paper based on a significant amount of field data taken on several Siemens gas turbines in operation. The field data refer to six different physical quantities, i.e. vibration, pressure, temperature, VGV position, lube oil tank level and rotational speed. The analyses carried out in this paper allow the detection and classification of the anomalies and provide some rules of thumb for field operation, with the final aim of identifying time occurrence and magnitude of faulty sensors and measurements.

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.

scholarly journals Monitoring and diagnostics of special-purpose turbomachines

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Małgorzata Gizelska
Keyword(s):  
Real Time ◽  
Measurement Data ◽  
Magnetic Bearing ◽  
Operating Conditions ◽  
Active Magnetic Bearing ◽  
Diagnostic Tools ◽  
Compression Process ◽  
Rotating System ◽  
On Line ◽  
Monitoring And Diagnostics

Abstract In the operation of special-purpose turbomachines, diagnostic tools are necessary. They enable control of the machine technical state and its operation parameters in the on-line mode. The acquisition and processing of the measurement data in real time is crucial as they are indicators of the machine functioning under various operating conditions. The paper presents two types of computer designed diagnostic tools to monitor in real time the dynamic and thermodynamic parameters of special-purpose turbomachines. The first one monitors the dynamics of the rotating system with an active magnetic bearing, the second - monitors the instant value of polytropic efficiency of the compression process, which was designed for the industrial machine.

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.

Improved Method for Gas-Turbine Off-Design Performance Adaptation Based on Field Data

2020 ◽  
Vol 142 (4) ◽  
Author(s):  
Shiyao Li ◽  
Zhenlin Li ◽  
Shuying Li
Keyword(s):  
Gas Turbine ◽  
Error Control ◽  
Field Data ◽  
Operating Conditions ◽  
Performance Parameters ◽  
Improved Method ◽  
Design Performance ◽  
Average Prediction Error ◽  
Comparison Results ◽  
Performance Adaptation

Abstract Obtaining accurate components' characteristic maps has great significant for gas-turbine operating optimization and gas-path fault diagnosis. A common approach is to modify the original components' characteristic maps by introducing correction factors, which is known as performance adaptation. Among the existing methods, total average prediction error of measurable parameters (MPTAPE) at specified conditions is used to evaluate the adaptation accuracy. However, when a gas turbine undergoes a field operation, the performance parameters of each component are zonally distributed under the operating conditions. Under such circumstances, randomly selecting a few data points as the error control points (ECPs) for performance adaptation may lead to an inappropriate correction of the characteristic maps, further lowering the prediction accuracy of the simulation model. In this paper, a genetic-algorithm-based improved performance adaptation method is proposed, which provides improvements in two aspects. In one aspect, similarity between the components' predicted performance curves and the performance regression curves is used as the criterion with which to evaluate the adaptation accuracy. In the other aspect, in the process of off-design performance adaptation, the performance parameters at the design point are recalibrated. The improved method has been verified by using rig test data and applied to field data of a GE LM2500+SAC gas turbine. The comparison results show that the improved method can obtain more accurate and stable adaptation results, while the computational load can be significantly reduced.

Gas Turbine Diagnostics Under Variable Operating Conditions

2007 ◽  
Author(s):  
Igor Loboda ◽  
Yakov Feldshteyn ◽  
Sergiy Yepifanov
Keyword(s):  
Gas Turbine ◽  
Health Monitoring ◽  
Correct Diagnosis ◽  
Diagnostic System ◽  
Diagnostic Technique ◽  
Operating Conditions ◽  
Diagnostic Process ◽  
Ambient Conditions ◽  
Operating Points ◽  
Variable Operating Conditions

Operating conditions (control variables and ambient conditions) of gas turbine plants and engines vary considerably. The fact that health monitoring has to be uninterrupted creates the need for a run time diagnostic system to operate under any conditions. The diagnostic technique described in this paper utilizes the thermodynamic models in order to simulate gaspath faults and uses neural networks for the faults localization. This technique is repeatedly executed and the diagnoses are registered. On the basis of these diagnoses and beforehand known faults, the correct diagnosis probabilities are then calculated. The present paper analyses the influence of the operating conditions on a diagnostic process. In the technique, different options are simulated of a diagnostic treatment of the measured values obtained under variable operating conditions. The mentioned above probabilities help to compare these options. The main focus of the paper is on the so called multipoint (multimode) diagnosis that groups the data from different operating points (modes) to set only a single diagnosis.

Computation and Monitoring of the Deviations of Gas Turbine Unmeasured Parameters

2015 ◽  
Author(s):  
Luis Angel Miro Zarate ◽  
Igor Loboda
Keyword(s):  
Gas Turbine ◽  
Thermodynamic Model ◽  
Real Data ◽  
Operating Conditions ◽  
Inlet Temperature ◽  
Engine Thrust ◽  
Shaft Power ◽  
On Line ◽  
Monitoring And Diagnostics ◽  
Computer Resources

One of the principle purposes of gas turbine diagnostics is the estimation and monitoring of important unmeasured quantities such as engine thrust, shaft power, and engine component efficiencies. There are simple methods that allow computing the unmeasured parameters using measured variables and gas turbine thermodynamics. However, these parameters are not good diagnostic indices because they strongly depend on engine operating conditions but in a less degree are influenced by engine degradation and faults. In the case of measured gas path variables, deviations between measurements and an engine steady state baseline were found to be good indicators of engine health. In this paper, the deviation computation and monitoring are extended to the unmeasured parameters. To verify this idea, the deviations of compressor and turbine efficiencies as well as a high pressure turbine inlet temperature are examined. Deviation computations were performed at steady states for both baseline and faulty engine conditions using a nonlinear thermodynamic model and real data. These computational experiments validate the utility of the deviations of unmeasured variables for gas turbine monitoring and diagnostics. The thermodynamic model is used in this paper only to generate data, and the proposed algorithm for computing the deviations of unmeasured parameter can be considered to be a data-driven technique. This is why the algorithm is not affected by inaccuracies of a physics-based model, is not exigent to computer resources, and can be used in on-line monitoring systems.

Simulation of an Advanced Twin-Spool Industrial Gas Turbine

1992 ◽  
Vol 114 (2) ◽  
pp. 180-185 ◽  
Author(s):  
Ping Zhu ◽  
H. I. H. Saravanamuttoo
Keyword(s):  
Mathematical Model ◽  
Gas Turbine ◽  
Excellent Agreement ◽  
Field Data ◽  
Full Range ◽  
Operating Conditions ◽  
New Method ◽  
Operating Point ◽  
Design Performance ◽  
Industrial Gas Turbine

A full-range mathematical model of the LM-1600 gas turbine has been developed, for future use in EHM studies. No data were available from the manufacturer other than sales brochures giving some design and off-design performance. The model was developed using generalized component characteristics and shows excellent agreement with field data from a pipeline operator. A new method has been developed for doing the matching calculations, starting from the turbine (hot) end rather than from the compressor operating point. This method permits solution on a PC, and can be used for studying the full range of operating conditions and the development of fault matrices.

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