Performance Model ìZoomingî For In-Depth Component Fault Diagnosis

Authors:Aretakis N., Roumeliotis I., Mathioudakis K.

Abstract

A method giving the possibility for a more detailed gas path component fault diagnosis, by exploiting the "zooming" feature of current performance modelling techniques, is presented. A diagnostic engine performance model is the main tool that points to the faulty engine component. A diagnostic component model is then used to identify the fault. The method is demonstrated on the case of compressor faults. A 1-D model based on the "stage stacking" approach is used to "zoom" into the compressors, supporting a 0-D engine model. A first level diagnosis determines the deviation of overall compressor performance parameters, while "zooming" calculations allow a localization of the faulty stages of a multistage compressor. The possibility to derive more detailed information with no additional measurement data is established, by incorporation of empirical knowledge on the type of faults that are usually encountered in practice. Although the approach is based on known individual diagnostic methods, it is demonstrated that the integrated formulation provides not only higher effectiveness but also additional fault identification capabilities.

Estimation of Gas Turbines Gradual Deterioration Through a Dempster-Schafer based Fusion Method

Authors:Romessis C., Mathioudakis K.

Abstract

This paper presents a fusion procedure of independently acting diagnostic methods, allowing gas turbines health condition assessment given a series of measurements. The proposed procedure incorporates a fusion technique, which is based on the Dempster-Schafer theory. The novel element of the method is its ability to cope with the problem of overall engine gradual performance deterioration, instead of identification of individual component fault events. The effectiveness of the technique is evaluated through its application on scenarios representing drifting gas turbine faults encountered in practice, using independently acting diagnostic methods, already established. Through this application, the efficiency of the proposed fusion procedure is demonstrated, along with the improvement it provides over its constituent methods to both the accuracy and the reliability of diagnosis.

Enhanced Fault Localization Using Probabilistic Fusion With Gas Path Analysis Algorithms

Authors:Kyriazis A., Mathioudakis K.

Abstract

A method for gas turbine fault identification from gas path data, in situations with a limited number of measurements,is presented. The method consists of a two stage process: (a) localization of the component or group of components where the fault is located and (b) fault identification, by determining the precise location and magnitude of component performance deviations. The paper focuses on methods that allow improved localization of the faulty components. Gas path analysis algorithms are applied to diagnostic sets comprising different combinations of engine components. The results are used to derive fault probabilities, which are then fused to derive a conclusion as to the location of a fault. Once the set of possible faulty components is determined, a well defined diagnostic problem is formulated and the faulty parameters are determined by means of a suitable algorithm. It is demonstrated that the method has an improved effectiveness when compared to previous GPA based methods.

Gas Turbine Fault Identification by Fusing Vibration Trending and Gas Path Analysis

Authors:Kyriazis A., Tsalavoutas A., Mathioudakis K., Bauer M., Johanssen O.

Abstract

A fusion method that utilizes performance data and vibration measurements for gas turbine component fault identification is presented. The proposed method operates during the diagnostic processing of available data (process level) and adopts the principles of certainty factors theory. Both performance and vibration measurements are analyzed separately, in a first step, and their results are transformed into a common form of probabilities. These forms are interweaved, in order to derive a set of possible faulty components prior to deriving a final diagnostic decision. Then, in the second step, a new diagnostic problem is formulated and a final set of faulty health parameters are defined with higher confidence. In the proposed method the non-linear gas path analysis is the core diagnostic method, while information provided by vibration measurements trends is used to narrow the domain of unknown health parameters and lead to a well defined solution. It is shown that the presented technique combines effectively different sources of information, by interpreting them into a common form and may lead to improved and safer diagnosis.

Gas Turbines Diagnostics Using Weighted Parallel Decision Fusion Framework

Authors:Kyriazis A., Mathioudakis K.

Abstract

A technique that allows the fusion of decisions provided by independent diagnostic methods for gas turbines faults is presented. It utilizes a Bayesian Belief Network to handle the various diagnostic decisions and provides a final assessment about the engine health condition. This task is performed in a parallel fusion framework with the optional addition of a weighting module, in two variants, for further diagnostic enhancement. Effectiveness of the proposed technique is illustrated by application to the detection of mechanical and aerothermodynamic component faults. In the latter case diagnostic conclusions of GPA (Gas Path Analysis) methods are merged delivering an improved diagnosis. In the case of mechanical faults, a radial compressor is examined by utilization of fast response data and diagnostic decisions are also combined for final diagnosis. The proposed method exhibits broad generality by utilizing different sources of information and fault scenarios alongside with improved diagnostic effectiveness.

Fusion of Gas Turbines Diagnostic Inference: The Dempster-Schafer Approach

Authors:Romessis C., Kyriazis A., Mathioudakis K.

Abstract

This paper proposes a fusion technique allowing the merge of conclusions provided by diagnostic methods that act independently for the detection of gas turbine faults. The proposed technique adopts the principles of Dempster-Schafer theory for the fusion of two diagnostic methods output; these are the method of Bayesian Belief Networks (BBN) and the method of Probabilistic Neural Networks (PNN). The proposed technique has been applied for the detection of thermodynamic as well as mechanical faults on gas turbines. First, the case of a turbofan engine of civil aviation is examined. The proposed technique allows the fusion of diagnostic inference on the presence of several faults of thermodynamic nature. Then the case of a radial and an axial compressor are examined, were several mechanical faults are deliberately implemented. In all cases, the effectiveness of the proposed fusion technique demonstrates that the merge of diagnostic information from different sources leads to better and safer diagnosis.

Detection of Gas Turbines Malfunctions From Emission Concentration Distributions

Authors:Romessis C., Mathioudakis K.

Abstract

A method for detecting gas turbines malfunctions through engine emissions concentration plots is presented. The method is materialized through the use of a bank of Probabilistic Neural Networks (PNNs). The main idea comes from the fact that specific operating and health conditions of an engine lead to specific concentrations of emissions on the exhaust area. By comparison of an emission concentration plot with emission plots of known engine health conditions, diagnostic conclusions can be extracted. The stochastic nature of emission concentrations can be handled by PNNs, a specific type of Artificial Neural Networks which are known to be efficient probabilistic classifiers. The diagnostic problem and the overall diagnostic procedure are first described. A detailed description of the way the diagnostic procedure is set-up, with focus on building the PNNs, follows. The case of an operating family of turbofan engines is used to evaluate the effectiveness of the method. The examined case demonstrates that the proposed method can act as an additional tool on the existing methods for better and safer fault diagnosis.

The Use of Probabilistic Reasoning to Improve Least Squares Based Gas Path Diagnostics

Authors:Romessis C., Kamboukos Ph., Mathioudakis K.

Abstract

A method is proposed to support least square type of methods for deriving health parameters from a small number of independent gas path measurements. The method derives statistical information using sets of solutions derived from a number of data records, to produce sets of candidate solutions with a lesser number of parameters. These sets can then be processed to derive an accurate component fault diagnosis. It could thus be classified as a new type of "concentrator" approach, which is shown to be more effective than previously existing schemes. The methods effectiveness is demonstrated by application to a number of typical jet engine component faults.

Turbofan Engine Health Assessment by Combining Steady and Transient State Aerothermal Data

Authors:Kamboukos Ph., Mathioudakis K.

Abstract

Operating aircraft engines are usually equipped with a limited number of sensors. This situation is a common issue in gas turbine diagnostics, where the absence of measurements from the engine gas path reduces the effectiveness of the applied methods. In order to overcome this problem, the exploitation of transient state measurement data for aerothermodynamic diagnosis has been proposed. Although such an approach seems to be a reasonable decision, the advantages and disadvantages of this method have not been clearly described. In addition the crucial issue of information independence has not been effectively addressed. The method introduced in the paper combines the use of steady and transient state measurement data. Initially a number of additional measurements are defined from the transient data. These measurements are based on the characteristic times of transient response which are used in control theory. It is demonstrated that the additional measurements exhibit significant deviations when the engine components deviate from the reference state. Thus it can be further used for the formulation of a diagnostic procedure. The validation of the method is performed using as a test case scenario a limited instrumented turbofan. Issues related with the implementation of the proposed method in conjunction with the computational cost are briefly discussed.

A Possibility for On-Board Training for Marine Gas Turbine Performance Monitoring and Diagnostics

Authors:Mathioudakis K., Aretakis N., Yfantis E.

Abstract

The paper discusses how performance models can be used for on board training for marine gas turbines in the field of performance monitoring and diagnostics. A particular performance model, built for on board training purposes is employed to demonstrate the different aspects of this process. The model allows the presentation of basic rules of gas turbine engine behavior and helps understanding different aspects of its operation. A smart designed graphics user interface is used to present engine operation in different ways: operating line, operating points of the components, interrelation between performance variables and parameters etc. It can be used for studying engine operation for both healthy and faulty cases featuring a novel approach, compared to existing simulation programs. The Faults can be easily implanted into different engine components and their impact on engine performance studied. The perception of fault signatures on monitoring parameters is clearly demonstrated. Diagnostics capabilities can also be incorporated in the model, allowing the introduction of measurement data from engines of unknown condition and providing a diagnosis, namely a picture of how the performance of engine components has deviated from a healthy condition.

Multipoint Non-Linear Method for Enhanced Component and Sensor Malfunction Diagnosis

Authors:Kamboukos Ph., Mathioudakis K.

Abstract

Operating gas turbine engines are usually equipped with a limited number of sensors. This situation is the common issue of gas turbine diagnostics where the absence of sufficient measurements from the engine gas path reduces the effectiveness of the applied methods. In addition the installed sensors of the engine deteriorate with time or present abrupt malfunctions which are not always detectable. One way to overcome this problem is the exploitation of information from a number of different operating points by constructing a multipoint diagnostic procedure. Information from different operating points is combined in order to increase the number of measurements and thus to form a well determined diagnostic system for the estimation of engine component health parameters. The paper presents the extension of the method in order to be able to assess both engine and sensors state. Initially the ability of the method to estimate the condition of a high bypass turbofan engine, exploiting information from different instances of its flight envelop is depicted. The problem of selecting the appropriate operating points is analyzed on the basis of the numerical condition of the formed diagnostic system. The method is also applied to a single shaft turbojet, for estimation of engine component health parameters and sensors state. Finally a number of aspects related to the formulation of the method are examined. These are the comparison between full method and its linear approximation, the effect of measurement noise on the derived estimation and the computational cost.

Combining Classification Techniques With Kalman Filters For Aircraft Engine Diagnostics

Authors:Dewallef P., Romessis C., Leonard O., Mathioudakis K.

Abstract

A diagnostic method consisting of a combination of Kalman filters and Bayesian Belief Networks (BBN) is presented. A soft constrained Kalman filter uses a priori information derived by a BBN at each time step, to derive estimations of the unknown health parameters. The resulting algorithm has improved identification capability in comparison to the stand alone Kalman filter. The paper focuses on the way of combining the information produced by the BBN with the Kalman filter. An extensive set of fault cases is used to test the method, on a typical civil turbofan layout. The effectiveness of the method is thus demonstrated and its advantages over individual constituent methods are shown.

Bayesian Network Approach for Gas Path Fault Diagnosis

Authors:Romessis C., Mathioudakis K.

Abstract

A method for solving the gas path analysis problem of jet engine diagnostics based on a probabilistic approach is presented. The method is materialized through the use of a Bayesian Belief Network (BBN). Building a BBN for gas turbine performance fault diagnosis requires information of a stochastic nature expressing the probability of whether a series of events occurred or not. This information can be extracted by a deterministic model and does not depend on hard to find flight data of different faulty operations of the engine. The diagnostic problem and the overall diagnostic procedure are first described. A detailed description of the way the diagnostic procedure is set-up, with focus on building the BBN from an engine performance model, follows. The case of a turbofan engine is used to evaluate the effectiveness of the method. Several simulated and benchmark fault case scenarios have been considered for this reason. The examined cases demonstrate that the proposed BBN-based diagnostic method composes a powerful tool. This work also shows that building a diagnostic tool, based on information provided by an engine performance model, is feasible and can be efficient as well.

Assessment of the Effectiveness of Gas Path Diagnostic Schemes

Authors:Mathioudakis K., Kamboukos Ph.

Abstract

A variety of methods can be used for the diagnosis of faults in gas path components of gas turbines. Problems that are common for diagnostic method implementation are the choice of measured quantities, choice of health parameters and choice of operating conditions for data retrieval. The present paper introduces some general principles for evaluation of the effectiveness of different diagnostic schemes. They encompass criteria proposed in past publications, while they offer additional possibilities for assessment of diagnostic effectiveness in various situations. The method is based on the evaluation of the behavior of linear systems, which are a good approximation of the non-linear ones for small deviations and employs the concept of system condition number to formulate criteria. The determination of limits for this number for establishing system condition criteria and quantification of observability is examined, on the basis of uncertainty propagation. Sample problems evaluated are: maximizing effectiveness of individual component identification from a multiplicity of available measurements, selection of individual operating points for multi-point applications.

Diagnostic methods and their application for the efficient management of gas turbines

Authors:Mathioudakis K., Aretakis N., Tsalavoutas A.

Abstract

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Implementation of Stochastic Methods For Industrial Gas Turbine Fault Diagnosis

Authors:Romessis C., Mathioudakis K.

Abstract

Implementation of stochastic diagnostic methods for diagnosis of sensor or component faults is presented. Two industrial gas turbines are considered as test cases, one twin and one single shaft arrangement. Methods based on Probabilistic Neural Networks (PNN) and Bayesian Belief Networks (BBN), are implemented. The ability for successful diagnosis is demonstrated on specific cases of sensor malfunctions, as well as on two types of compressor deterioration, fouling and variable vane mistuning. The examined diagnostic problem and the methods of PNN for sensor fault diagnosis and BBN for the diagnosis of component faults are first described. For each gas turbine case, the implementation of the diagnostic methods is shown and application to fault cases that occurred is presented. The effectiveness of the stochastic diagnostic methods demonstrates that they offer a powerful alternative diagnostic tool.

Comparison of Linear And Non-Linear Gas Turbine Performance Diagnostics

Authors:Kamboukos Ph., Mathioudakis K.

Abstract

The features of linear performance diagnostic methods are discussed, in comparison to methods based on full non-linear calculation of performance deviations, for the purpose of condition monitoring and diagnostics. First, the theoretical background of linear methods is overviewed to establish a relationship to the principles used by non-linear methods. Then computational procedures are discussed and compared. The effectiveness of determining component performance deviations by the two types of approaches is examined, on different types of diagnostic situations. A way of establishing criteria to define whether non-linear methods have to be employed is presented. An overall assessment of merits or weaknesses of the two types of methods is attempted, based on the results presented in the paper.

Gas Turbine Component Fault Detection from a Limited Number of Measurements

Authors:Kamboukos Ph., Stamatis A., Mathioudakis K.

Abstract

A method for detecting faults in the components of gas turbines, based on the use of non-linear engine models and optimization techniques, is presented. The method determines deviations in mass flow capacity and efficiency of individual engine components through minimization of appropriate cost function, formulated such that measurements are matched in an optimum way. Component performance deviations are expressed through appropriate modification factors, which are used as health parameters. The modification factors are coupled to a non-linear engine performance model and can represent different health conditions of the engine. The problem of fault diagnosis is formulated as the problem of determining the values of these factors from a given set of measurement data. The novel aspect of the method presented in this paper is that it can be used to determine health factors that are less, equal or larger in number than the available performance measurements. When measurements are fewer than the parameters to be determined, solutions are derived using an approach of the maximum likelihood type. It is demonstrated than such a solution can provide successful diagnosis for the majority of fault types expected to occur in an engine. The method presented is substantiated by application to a large bypass ratio, partially mixed, turbofan, typical of the large civil aircraft engine configuration in todayís aircrafts. An extensive set of component faults is studied, representing malfunctions expected to occur in practice. The method is shown to perform successfully in fault identification over this set, using a limited number of measurements representative of current onboard instrumentation.

On-Line Aircraft Engine Diagnostic Using A Soft-Constrained Kalman Filter

Authors:Dewallef P., Leonard O., Mathioudakis K.

Abstract

The purpose of this contribution is to apply ridge regression to Kalman filtering in order to stabilize a health parameter identification under low or negative redundancy. The resulting algorithm achieves a so-called soft-constrained recursive health parameter identification, i.e. constraints are applied to parameters in a statistical way, contrary to hard-constrained algorithms based on strong equality or inequality constrains. The method is tested on data generated by a steady state turbofan engine model and representing typical component faults. The benefits that can be realized in terms of stability and accuracy are highlighted and some limits of the method are also mentioned.

Optimization Methods in Gas Turbine Performance Diagnostics

Authors:Kamboukos Ph., Mathioudakis K.

Abstract

The comparison of different optimization methods used for gas turbine engine condition diagnosis is presented. Accuracy and speed of the examined algorithms is examined. The study is performed using a general form of objective function, which covers known functions presented in the past by several authors. According to the diagnostic situation faced an appropriate function type is examined. The case where a large number of health parameters needs to be determined from a smaller number of measurements receives more attention. Some particular aspects of the optimization procedures are discussed in the light of the results presented in the paper.

A Comparative Study Of Optimization Methods For Jet Engine Condition Diagnosis

Authors:Kamboukos Ph., Mathioudakis K., Stamatis A.

Abstract

The comparison of different optimization methods used for jet engine condition diagnosis is presented. Accuracy and speed of the examined algorithms is examined. The study is performed using a general form of objective function, which covers known functions presented in the past by several authors. According to the diagnostic situation faced an appropriate function type is examined. The case where a large number of health parameters needs to be determined from a smaller number of measurements receives more attention. The effect of measurement noise to the performance of diagnostic approaches using optimization algorithms is also examined. Some particular aspects of the optimization procedures are discussed in the light of the results presented in the paper.

Non-Linear Engine Component Fault Diagnosis From A Limited Number Of Measurements Using A Combinatorial Approach

Authors:Aretakis N., Mathioudakis K., Stamatis A.

Abstract

A method for diagnosing component faults of jet engines is presented. It uses non-linear gas path analysis techniques to determine the values of health parameters, with the help of a suitably formulated engine simulation model. The incentive of the method is to achieve the determination of the values of component health indices when a limited number of measured quantities is available, which do not allow the determination of all the fault indices simultaneously. A combinatorial approach is introduced, in order to circumvent the problem of the insufficient information for determining a full set of indices. After obtaining a set of possible solutions, a selection procedure is applied to isolate the ones that can give the actual fault identity. Quantification of the fault comes at a final step, when the faulty component has been identified. Different scenarios of faults on a twin spool partially mixed turbofan engine are considered in order to demonstrate the effectiveness of the method. The limitations of the method are also discussed.

Turbofan Fault Detection Using Non-Linear Models and Optimization Techniques

Authors:Kamboukos Ph., Stamatis A., Mathioudakis K.

Abstract

A method of detecting faults in the components of jet engines, based on the use of non- linear engine models and optimization techniques is presented. The method determines deviations in mass flow capacity and efficiency of individual engine components, through minimization of appropriate cost function, formulated in such a way that measurements are matched in an optimum way. Components performance deviations are expressed through appropriate modification factors, which are used as health parameters. The modification factors are coupled to non-linear engine performance model and can represent different health conditions of the engine. The problem of fault diagnosis is formulated as the problem of determination of the values of these factors from a given set of measurement data. The novel aspect of the method presented in this paper is that it can be used to determine health factors that are less, equal or larger in number than the available performance measurements. When measurements are fewer than parameters to determine, solutions are derived on an approach of the maximum likelihood type. It is demonstrated than such solution can provide successful diagnosis for the majority of fault types expected to occur in an aeroengine. The method presented is substantiated by application to a large bypass ratio, partially mixed, turbofan, typical of large civil aircraft engines configuration in today's aircrafts.

Increasing Diagnostic Effectiveness By Inclusion Of Fuel Composition And Water Injection Effects

Authors:Mathioudakis K., Aretakis N., Tsalavoutas A.

Abstract

The paper presents an analysis of the effect of changing the fuel on the performance of industrial gas turbines and examines the impact of such a change on methods used for engine condition assessment and fault diagnostics. A similar analysis is presented for the effects of water injection in the combustion chamber (which is usually done for reducing NOx emissions). First, the way of incorporating the effect of fuel changes and water injection into a computer model of gas turbine performance is described. The approach employed is based on the change of (a) working fluid properties, (b) turbomachinery components performance. The model is then used to derive parameters indicative of the "health" of a gas turbine and thus diagnose the presence of deterioration or faults. The impact of ignoring the presence of an altered fuel or injected water is shown to be of a magnitude that would render a diagnostic technique that does not incorporate these effects ineffective. On the other hand, employing the appropriate physical modeling makes the diagnostic methods robust and insensitive to such changes, being thus able to provide useful diagnostic information continuously during the use of a gas turbine.

Uncertainty Reduction in Gas Turbine Performance Diagnostics by Accounting for Humidity Effects

Authors:Mathioudakis K.,Tsalavoutas A.

Abstract

The paper presents an analysis of the effect of ambient humidity on the performance of industrial gas turbines and examines the impact of humidity on methods used for engine condition assessment and fault diagnostics. First, the way of incorporating the effect of humidity into a computer model of gas turbine performance is described. The model is then used to derive parameters indicative of the "health" of a gas turbine and thus diagnose the presence of deterioration or faults. The impact of humidity magnitude on the values of these health parameters is studied and the uncertainty introduced, if humidity is not taken into account, is assessed. It is shown that the magnitude of the effect of humidity depends on ambient conditions and is more severe for higher ambient temperatures. Data from an industrial gas turbine are presented to demonstrate these effects and to show that if humidity is appropriately taken into account, the uncertainty in the estimation of health parameters is reduced.

Allocating the Causes of Performance Deterioration in CCGT Plants

Authors:Mathioudakis K., Stamatis A., Bonataki E.

Abstract

A method for defining which parts of a combined cycle gas turbine (CCGT) power plant are responsible for performance deviations is presented. When the overall performances deviate from their baseline values, application of the method allows the determination of the component(s) of the plant, responsible for this deviation. It is shown that simple differentiation approaches may lead to erroneous conclusions, because they do not reveal the nature of deviations for individual components. Contributions of individual components are then assessed by separating deviations due to permanent changes and deviations due to change of operating conditions. A generalized formulation is presented together with the way of implementing it. Test cases are given, to make clearer the ideas put forward in the proposed method.

Setting up a Belief Network for Turbofan Diagnosis With the Aid of an Engine Performance Model

Authors:Romessis C., Stamatis A., Mathioudakis K.

Abstract

This paper presents a method for building Bayesian Belief Networks (BBN) for gas turbine performance fault diagnosis. Building a BBN requires information of stochastic nature. It is a common practice to extract this kind of information from statistical analysis of large data sets. In the field of gas turbine diagnostics, though, such data are usually hard to find. With the present method, the required information is extracted from an engine performance model. In this way, stochastic information, expressing the probability of whether a series of events occurred or not, can be extracted by a deterministic model and does not depend on, hard to find, flight data of different faulty operations of the engine. The diagnostic problem is first described. Some basic concepts of BBN, though briefly described, are also presented in relation to turbofan engines. A detailed description of the proposed way to set-up a diagnostic BBN from an engine performance model follows. Several simulated, but realistic, fault cases are then used for inference with the constructed network. Inference with BBN showed that such a network is very reliable, since in the 96% of the cases where a fault was detected, it was detected correctly. Only a 4% of the cases was attributed to a wrong fault. In some cases, the network was not ësensitiveí in the presence of a fault, since it did not detect any fault at all. Further, preliminary work, though, shows that the ësensitivityí of the network can be increased. It is shown that building a BBN, based on information provided by an engine performance model, is feasible and can be efficient as well.

Optimizing Diagnostic Effectiveness of Mixed Turbofans by Means of Adaptive Modelling and Choice of Appropriate Monitoring Parameters

Authors:Kamboukos Ph., Oikonomou P., Stamatis A., Mathioudakis K.

Abstract

Methods for the optimal selection of measurements and health parameters used for diagnostic purposes in aircraft gas turbine engines are presented. Principles of aerothermodynamic diagnostic techniques are first briefly reviewed. The problem of optimal selection of measurements and health parameters is examined from two different standpoints. (a) How to select out of all available measurements the minimum set that will be capable to provide sufficient information to assess engine health condition. (b) When a set of measurable quantities from an operating engine is given, how to select the combination of health parameters, in order to provide in an optimal way the information about the condition of the engine. The present paper concentrates mainly on the second type of problem since it is related to the handling of an existing fleet. Methods based on sensitivity analysis are discussed, but it is shown that the most substantial information is produced by analyzing the properties of the Jacobian matrix, interrelating parameters and measurement deviations. Finally, results of condition estimation for a number of turbofans in service are presented.

A Parametric Investigation of the Diagnostic Ability of Probabilistic Neural Networks on Turbofan Engines

Authors:Romessis C., Stamatis A., Mathioudakis K.

Abstract

Fault identification through the use of Artificial Neural Networks has become very popular recently. Probabilistic Neural Networks (PNN) is one of the architectures, which have mostly been investigated for gas turbine diagnostics. In this paper, the influence of parameters related to the structure and training on the diagnostic performance of a probabilistic Neural Network (PNN), is investigated. In particular, the parametric investigation examines the effect of the training set on the diagnostic performance of a PNN. The effect of noise level was also examined and found to be important. Another parameter examined is the severity of a fault, which was found to affect seriously the performance of the diagnostic PNN. Other parameters also examined are the effect of the operating conditions as well as the considered output parameters of the network. Guidelines useful for setting up this type of network, are derived.

Identifying Faults in the Variable Geometry System of a Gas Turbine Compressor

Authors:Tsalavoutas A., Mathioudakis K., Stamatis A., Smith M.K.

Abstract

The influence of faults in the variable geometry (variable stator vanes) system of a multistage axial compressor, on the performance of an industrial gas turbine is investigated. An experimental investigation has been conducted, by implanting such faults into an operating gas turbine. The faults examined are individual stator vane mistuning of different magnitude, and located at different stages. Fault identification is based on the aerothermodynamic measurement data and is achieved by employing two different techniques, namely adaptive performance modelling and monitoring the circumferential distribution of the turbine exit temperature. It is observed that the deviations of the modification factors, introduced to an adaptive performance model, form patterns that can be used to identify the faults. The patterns characterize both the kind and the magnitude of the fault. On the other hand, the turbine exit temperature profile is also influenced and its change can be used as additional information, to increase the confidence level of the diagnosis (contrary to customary practice, which expects temperatures profiles to reflect only burner or turbine malfunctions).

On-Line Leak Monitoring in Fluid Pumping Systems

Authors:Pouliezos A., Stavrakakis G., Mathioudakis K.

Abstract

In this paper, a model-based leak detection methodology for fluid pumping systems is developed. The novelty of this approach lies in modelling the leak position as a point between a real and an imaginary valve. The equations that describe the resulting dynamic system are then put into an input-output form suitable for least squares estimation. In this way, classic parameter-estimation based detection methods are applied to moving windows of system data. Computer simulation illustrates the feasibility of the method.

Instructing the Principles of Gas Turbine Performance Monitoring and Diagnostics by Means of Interactive Computer Models

Authors:Mathioudakis K., Stamatis A., Tsalavoutas A., Aretakis N.

Abstract

The paper discusses how the principles employed for monitoring the performance of gas turbines in industrial duty can be explained by using suitable Gas Turbine performance models. A particular performance model that can be used for educational purposes is presented. The model allows the presentation of basic rules of gas turbine engine behaviour and helps the understanding different aspects of its operation. It is equipped with a graphics interface, so it can present engine operating point data in a number of different ways: operating line, operating points of the components, variation of particular quantities with operating conditions etc. Its novel feature, compared to existing simulation programs, is that it can be used for studying cases of faulty engine operation. Faults can be implanted into different engine components and their impact on engine performance studied. The notion of fault signatures on measured quantities is cleary demonstrated. On the other hand, the model has 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 nominal condition, and how this information gives the possibility for fault identification.

Combining Advanced Data Analysis Methods for the Constitution of an Integrated Gas Turbine Condition Monitoring and Diagnostic System

Authors:Tsalavoutas A., Aretakis N., Stamatis A., Mathioudakis K.

Abstract

This paper presents principles for the constitution of gas turbine monitoring and diagnostic systems which: a. are integrated, namely manage all the tasks essential for achieving a diagnosis (measurement, analysis, interpretation, historical data management etc.) b. employ different kind of processing methods in order to cover an extensive range of engine conditions, including direct data evaluation and data consistency checks, thermodynamic analysis, vibration analysis. The requirements to be fulfilled by an industrial gas turbine monitoring system are briefly reviewed and ways to achieve them are discussed, indicating how they can be materialized by implementation of specific techniques. Techniques previously derived by the group of the authors are implemented, and the merits they offer when used in combination are discussed. Features of a system, materialized according to the principles discussed, into an operating industrial gas turbine is presented. On-line application of advanced analysis techniques, such as adaptive performance modeling is discussed, on the basis of observations of the collected data. Data collected from an engine operating in the field are presented to substantiate the matters discussed, and cases of successful fault identification are shown.

Jet Engine Component Maps for Performance Modelling and Diagnostics

Authors:Sieros G., Stamatis A., Mathioudakis K.

Abstract

This paper describes an effort to model the performance maps of compressors and turbines (i.e., the relation between mass è ow, pressure ratio, and efé ciency), using analytical functions. Analytical functions are fitted to the available experimental data using a least-squares-type approach for determining the parameters of the é tting function. The success of using a particular function for an application is assessed through a suitably deé ned mean error of the model. Apart from presenting the method for setting up these analytical representations, applications to performance modeling and fault diagnosis are discussed. The change in model parameters is used to characterize changes of the engine condition and possibly diagnose occurring faults. The impact of introducing analytical component models into overall engine computer models, replacing a tabulated form of the component maps, is also discussed.

Adaptive Modelling of Jet Engine Performance With Application to Condition Monitoring

Authors:Lambiris B., Mathioudakis K., Stamatis A., Papailiou K.D.

Abstract

A method of simulation of the performance of jet engines, with the possibility of adapting to engine particularities, is presented. It employs an adaptation procedure coupled to a performance model solving the component matching problem. The proposed method can provide accurate simulation for engines of the same type, with differences that are due to manufacturing or assembly tolerances. It does not require accurate component maps, because they are derived during the adaptation procedure. It can also be used for health monitoring purposes, for component fault identification, and condition assessment. The effectiveness of the proposed method is demonstrated by application to two commercial jet engines.

Optimal Measurements and Health Indices Selection for Gas Turbine Performance Status and Fault Diagnosis

Authors:Stamatis A., Mathioudakis K., Papailiou K.D.

Abstract

In this paper, we present a method for defining the health estimation parameters and the measurements that must be used when a monitoring system for an engine is being set up. The particular engine layout, the available measuring instruments, and the accuracy by which data can be collected are the factors taken into account. The particular health condition estimation factors that have to be used are defined as a function of this information and the desired depth of fault identification. A fast selection procedure based on the method of singular value decomposition is presented. The uncertainty in the estimations is also derived, thus giving an additional element of information useful for decision making. The proposed method, together with adaptive performance modeling, provides a self-sufficient tool, which can be applied for setting up and subsequent exploitation of a health monitoring expert system. The advantage of the procedure is that it provides a frame of application, allowing quick implementation in a new engine of interest, other than the ones previously considered.

Jet Engine Fault Detection with Discrete Operating Points Gas Path Analysis

Authors:Stamatis A., Mathioudakis K., Berios K., Papailiou K.D.

Abstract

A common feature of all Differential Gas Path Analysis methods is the necessity of measuring a number of performance variables greater or at least equal to the number of diagnostic parameters which have to be estimated. Discrete Operating Conditions Gas Path Analysis (DOCGPA) is an extended version of the conventional GPA algorithms, providing-among other things-the capability to overcome this problem. In the present paper, we describe how this method can be coupled with an engine computer model, in order to perform component directed fault diagnosis. Application to a commercial turbofan engine demonstrates the effectiveness of the proposed method.

Computer Modelling and Data Processing Methods. An Essential Part of Jet Engine Condition Monitoring and Fault Diagnosis

Authors:Mathioudakis K., Stamatis A., Loukis E., Papailiou K.D.

Abstract

Methods of processing measurement data in order to derive information about the health of an engine are presented. Both aerothermodynamic performance and fast response measurement data are covered. The principles and representative results of an advanced Gas Path Analysis method are given. The method of Adaptive Performance Modeling and its application to fault diagnosis is described. Identification of faults on rotating blades by using unsteady pressure measurements is discussed. Extraction of diagnostic information from casing acceleration as well as acoustic measurements is also discussed.

Compressor Washing Economic Analysis and Optimization for Power Generation

Authors:Aretakis N., Roumeliotis I., Doumouras G., Mathioudakis K.

Abstract

The deregulation of the energy market has created an additional incentive for gas turbine plants operators to minimize and control performance deterioration with respect to the economical aspects of the plant. The most prevalent deterioration problem is compressor fouling, which has a significant impact on the power plant profit. Off-line washing is able to recover the engine’s performance losses due to fouling, but has a variety of associated costs. A method to predict the impact of the compressor washing process on the power plant revenue is presented herein, allowing for the optimization of the process with regards to power plant specific data. For this reason, a detailed cost analysis module is formed and coupled with an engine model allowing for the study of both economic parameters and engine operation parameters like the increase of maintenance cost due to start-ups and the variation of the engine degradation rate. The method is applied for the case of an aeroderivative gas turbine of 42 MW. The parameters associated with the off-line washing process and the engine performance that affects the plant’s revenue are examined and discussed, while recommendations on the optimal washing schedule are made.

Modelling And Assessment Of Compressor Faults On Marine Gas Turbines

Authors:Roumeliotis I., Aretakis N., Mathioudakis K., Yfantis E.

Abstract

Any prime mover exhibits the effects of wear and tear over time, especially when operating in a hostile environment. Marine gas turbines operation in the hostile marine environment results in the degradation of their performance characteristics. A method for predicting the effects of common compressor degradation mechanisms on the engine operation and performance by exploiting the “zooming” feature of current performance modelling techniques is presented. Specifically a 0D engine performance model is coupled with a higher fidelity compressor model which is based on the “stage stacking” method. In this way the compressor faults can be simulated in a physical meaningful way and the overall engine performance and off design operation of a faulty engine can be predicted. The method is applied to the case of a twin shaft engine, a configuration that is commonly used for marine propulsion.

In the case of marine propulsion the operating profile includes a large portion of off-design operation, thus in order to assess the engine’s faults effects, the engine operation should be examined with respect to the marine vessel’s operation. For this reason, the engine performance model is coupled to a marine vessel’s mission model that evaluates the prime mover’s operating conditions. In this way the effect of a faulty engine on vessels’ mission parameters like overall fuel consumption, maximum speed, pollutant emissions and mission duration can be quantified.

Fault Diagnosis Of Thermal Turbomachines Using Support Vector Machines (SVM)

Authors:Kalathakis C., Romesis C.,Aretakis N., Mathioudakis K.

Abstract

A method for detecting gas turbines malfunctions through aerothermodynamic and fast response measurements is presented. The proposed method is based on Support Vector Machines (SVM), a modern classification technique in the field of machine learning and artificial intelligence.

A brief description of SVM theory is given first, followed by a description of the overall diagnostic procedure, assisted by an Engine Performance Model (EPM). The method is evaluated through its effectiveness on three realistic fault diagnosis test cases: a) a turbofan engine, b) an axial and a radial compressor and c) sensors of a turbofan engine.

Application on these test cases demonstrates that the proposed SVM-based diagnostic method allows more accurate and reliable fault diagnosis, compared to other modern techniques in the field of gas turbines diagnostics.

Turbofan Engine Health Assessment From Flight Data

Authors:Aretakis N., Roumeliotis I., Alexiou A., Romesis C., Mathioudakis K.

Abstract

The paper presents the use of different approaches to engine health assessment using on-wing data obtained over a year from an engine of a commercial short-range aircraft. The on-wing measurements are analyzed with three different approaches, two of which employ two models of different quality. Initially, the measurements are used as the sole source of information and are post-processed utilizing a simple “model” (a table of corrected parameter values at different engine power levels) to obtain diagnostic information. Next, suitable engine models are built utilizing a semi-automated method which allows for quick and efficient creation of engine models adapted to specific data. Two engine models are created, one based on publicly available data and one adapted to engine specific on-wing “healthy” data. These models of different detail are used in a specific diagnostic process employing model-based diagnostic methods, namely the Probabilistic Neural Network (PNN) method and the Deterioration Tracking method. The results demonstrate the level of diagnostic information that can be obtained for this set of data from each approach (raw data, generic engine model or adapted to measurements engine model). A sub-system fault is correctly identified utilizing the diagnostic process combined with the engine specific model while the Deterioration Tracking method provides additional information about engine deterioration.

Experience With Condition Based Maintenance Related Methods And Tools For Gas Turbines

Authors:Romesis C., Aretakis N., Roumeliotis I., Alexiou A., Tsalavoutas A., Stamatis A., Mathioudakis K.

Abstract

This paper presents methods and tools related to condition-based maintenance (CBM) and their application on a number of real cases for gas turbine health assessment based on the experience gained over the last two decades by the research group of the Laboratory of Thermal Turbomachines at the National Technical University of Athens (LTT/NTUA).

First, the general layout of a CBM system and its constituent parts are presented, followed by a description of several related methods that have been developed by LTT/NTUA. In addition to engine performance modeling techniques, these methods incorporate model-based, stochastic and artificial intelligence approaches that can be used for sensor validation, engine component fault diagnosis and compressor washing optimization. Many of these methods have been integrated into stand-alone, customized diagnostic tools in-service today featuring –among others –hot section monitoring, performance data analysis and vibration monitoring.

These techniques have been tested and validated against benchmark cases and implemented in a number of operating gas turbine engines that are presented in the paper.

The examined case studies demonstrate that advanced diagnostic methods can efficiently detect gas turbine malfunctions in an automated way and at an early stage of their appearance, both essential features in real-world applications.

Industrial Gas Turbine Health and Performance Assessment With Field Data

Authors: I. Roumeliotis, N. Aretakis, A. Alexiou

Abstract

The paper presents a thorough analysis of historical data and results acquired over a period of two years through an online 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.

Evaluation Of Aircraft Engine Gas Path Diagnostic Methods Through PRODIMES

Authors: O. Koskoletos, N. Aretakis, A. Alexiou, C. Romesis, K. Mathioudakis

Abstract

Propulsion Diagnostic Method Evaluation Strategy (ProDiMES) offers an aircraft engine diagnostic benchmark problem where the performance of candidate diagnostic methods is evaluated while a fair comparison can be established. In the present paper, the performance evaluation of a number of gas turbine diagnostic methods using the ProDiMES software is presented. All diagnostic methods presented here were developed at the Laboratory of Thermal Turbomachinery of the National Technical University of Athens (LTT/NTUA). Component, sensor and actuator fault scenarios, that occur in a fleet of deteriorated twin-spool turbofan engines are considered. The performance of each diagnostic method is presented through the evaluation metrics introduced in the ProDiMES software. Remarks about each methods performance as well as the detectability and classification rates of each fault scenario are made.

Integrated Simulation Framework for Assessing Turbocharger Fault Effects on Diesel-Engine Performance and Operability

Authors: Ntonas, K., Aretakis, N., Roumeliotis, I., Pariotis, E., Paraskevopoulos, Y., & Zannis, T.

Abstract

Turbocharged diesel engines are extensively used in marine vessels, both as propulsion engines and as generator sets. The engine’s operation in the hostile marine environment results in performance degradation having a negative effect on the economics of the marine vessel’s operation both in terms of fuel consumption and maintenance. This paper presents a turbocharged four-stroke diesel engine simulation framework based on one-dimensional calculations and analysis. The framework is suitable for turbomachinery and heat exchanger components fault simulation predicting both turbocharger and diesel engine performance and operability. Mean-line models were used in conjunction with the beta lines method for generating accurate and detailed compressor and turbine performance maps, coupled with a single zone closed-cycle diesel engine model for generating engine performance characteristics. The simulation framework modules are adjusted and validated against measured data. Following specific faults are simulated utilizing physical consistent parameters such as blade friction and thickness based on relevant literature data. Overall system simulation and operation analysis is carried out assessing operability and performance parameters. Analysis results show a significant reduction in engine performance, especially in case of both turbo components being fouled (22% power reduction), in contrast with the heat exchanger fouling where the power reduction is about 1%.

Application of an Advanced Adaptation Methodology for Gas Turbine Performance Monitoring

Authors: P. Rompokos, N.Aretakis, I. Roumeliotis, K. Mathioudakis

Abstract

Adaptive modelling diagnostic methods are valuable tools for gas turbine performance and condition monitoring. Component maps capable of accurate representation of engine operation are not available to the engine operators. In this context, adaptive methods can be used for tuning the component maps for simulating accurately the engine performance throughout the whole operating envelope. In most cases, the map tuning is performed at a single operating point or operating line and although scaled maps can provide accurate results close to the reference points, deviations may significantly increase away from these reference points. For industrial gas turbines, in the past, this was not a major issue, given that most engines operated at or close to baseload. Currently, due to the increased share of renewable power in the generation mix in Europe, there is a shift of industrial gas turbines operating mode from baseload to load following and part load, thus there is a need for accurately simulating engine operation in a broader operating range. This paper presents a component map adaptation methodology integrating several methods applied in the literature in six steps. The novel methodology is applied to on-engine measurements from a heavy-duty gas turbine and the betterment on simulation achieved from each step is quantified. The adapted component maps enable accurate engine condition monitoring as demonstrated by a second test case for an industrial twin shaft engine where operating data spanning over four months are assessed.