Publications
On the selection of an optimal pattern recognition technique for gas turbine diagnosis
Loboda Igor, Sergiy Yepifanov. On the selection of an optimal pattern recognition technique for gas turbine diagnosis, ASME Turbo Expo 2013: International Technical Congress “Power for Land Sea & Air”, San Antonio, Texas, USA, June 3-7, 2013, 11p., ASME Paper No. GT2013-95198.
Efficiency of gas turbine monitoring systems primarily depends on the accuracy of employed algorithms, in particular, pattern recognition techniques to diagnose gas path faults. In investigations many techniques were applied to recognize gas path faults, but recommendations on selecting the best technique for real monitoring systems are still insufficient and often contradictory.
In our previous works, three recognition techniques were compared under different conditions of gas turbine diagnosis. The comparative analysis has shown that all these techniques yield practically the same accuracy for each comparison case.
The present contribution considers a new recognition technique, Probabilistic Neural Network (PNN), comparing it with the techniques previously examined. The results for all comparison cases show that the PNN is not practically inferior to the other techniques. With this inference, the recommendation is to choose the PNN for real monitoring systems because it has an important advantage of providing confidence estimation for every diagnostic decision made.
Link to the publication »Ridge estimation and principal component analysis to solve an ill-conditioned problem of estimating unmeasured gas turbine parameters
Maksim Shevchenko, Sergiy Yepifanov, Loboda Igor. Ridge estimation and principal component analysis to solve an ill-conditioned problem of estimating unmeasured gas turbine parameters, ASME Turbo Expo 2013: International Technical Congress “Power for Land Sea & Air”, San Antonio, Texas, USA, June 3-7, 2013, 12p., ASME Paper No. GT2013-94496.
This paper addresses the problem of estimation of unmeasured gas turbine engine variables using statistical analysis of measured data. Possible changes of an engine health condition and lack of information about these changes caused by limited instrumentation are taken into account. Engine thrust is under consideration as one of the most important unmeasured parameters. Two common methods of aircraft gas turbine engine (GTE) thrust monitoring and their errors due to health condition changes are analyzed. Additionally, two mathematical techniques that allow reducing in-flight thrust estimation errors in the case of GTE deterioration are suggested and verified in the paper. They are a ridge trace and a principal component analysis.
A turbofan engine has been chosen as a test case. The engine has five measured variables and 23 health parameters to describe its health condition. Measurement errors are simulated using a generator of random numbers with the normal distribution. The engine is presented in calculations by its nonlinear component level model (CLM). Results of the comparison of thrust estimates computed by the CLM and the proposed techniques confirm accuracy of the techniques. The regression model on principal components has demonstrated the highest accuracy.
Link to the publication »A more realistic scheme of deviation error representation for gas turbine diagnostics
Loboda I., Yepifanov S., Feldshteyn Ya. A more realistic scheme of deviation error representation for gas turbine diagnostics / International Journal of Turbo & Jet Engines, Walter de Gruyter GmbH und Co. KG, Germany, 2013, Vol.30, Issue 2, pp. 179-189.
Gas turbine diagnostic algorithms widely use fault simulation schemes, in which measurement errors are usually given by theoretical random number distributions, like the Gaussian probability density function. The scatter of simulated noise is determined on the basis of known information on maximum errors for every sensor type. Such simulation differs from real diagnosis because instead of measurements themselves the diagnostic algorithms work with their deviations from an engine baseline. In addition to simulated measurement inaccuracy, the deviations computed for real data have other error components. In this way, simulated and real deviation errors differ by amplitude and distribution. As a result, simulation-based investigations might result in too optimistic conclusions on gas turbine diagnosis reliability.
To understand error features, deviations of real measurements are analyzed in the present paper. To make error presentation more realistic, it is proposed to extract an error component from real deviations and to integrate it in fault description. Finally, the effect of the new noise representation mode on diagnostic reliability is estimated. It is shown that the reliability change due to inexact error simulation can be significant.
Link to the publication »ASME Paper No. GT2012-69368
Link to the publication »Probability density estimation techniques for gas turbine diagnosis
І. Loboda. Probability density estimation techniques for gas turbine diagnosis / Aerospace Technics and Technology. Journal: National Aerospace University, Kharkov, Ukraine, 2013- Issue 6(103), pp. 53-59.
In gas turbine engine condition monitoring systems, diagnostic algorithms based on measured gas path variables constitute an important component. Not only gas path faults are diagnosed by these algorithms, but also malfunctions of sensors and an engine control system can be identified with gas path measurements. Many gas path diagnostic algorithms use pattern classification techniques. In particular, a specific neural network, Multilayer Perceptron (MLP), is mostly applied. Unfortunately, the MLP cannot provide confidence estimation for its diagnostic decisions. However, there are techniques that classify patterns on the basis of probability. For example, Parzen Window and K-Nearest Neighbor methods compute probabilities of the considered classes estimating their probability densities. Thus, every diagnosis made is accompanied by its probability that is a very useful property for real gas turbine diagnosis. In the present paper, these two techniques are compared with the MLP in order to determine the technique that provides the best diagnostic accuracy on average for all possible gas turbine faults. The mentioned advantage of the Parzen Windows and K-Nearest Neighbors is also taken into account.
Full text »Gas turbine fault classification using probability density estimation
Loboda Igor. Gas turbine fault classification using probability density estimation, ASME Turbo Expo 2014: International Technical Congress “Power for Land Sea & Air”, Dusseldorf, Germany, June 16-20, 2014, 13p., ASME Paper No. GT2014-27265.
Diagnostics is an important aspect of a condition based maintenance program. To develop an effective gas turbine monitoring system in short time, the recommendations on how to optimally design every system algorithm are required. This paper deals with choosing a proper fault classification technique for gas turbine monitoring systems.
To classify gas path faults, different artificial neural networks are typically employed. Among them the Multilayer Perceptron (MLP) is the mostly used. Some comparative studies referred to in the introduction show that the MLP and some other techniques yield practically the same classification accuracy on average for all faults. That is why in addition to the average accuracy, more criteria to choose the best technique are required. Since techniques like Probabilistic Neural Network (PNN), Parzen Window (PW) and k-Nearest Neighbor (K-NN) provide a confidence probability for every diagnostic decision, the presence of this important property can be such a criterion. The confidence probability in these techniques is computed through estimating a probability density for patterns of each concerned fault class.
The present study compares all mentioned techniques and their variations using as criteria both the average accuracy and availability of the confidence probability. To compute them for each technique, a special testing procedure simulates numerous diagnosis cycles corresponding to different fault classes and fault severities. In addition to the criteria themselves, criteria imprecision due to a finite number of the diagnosis cycles is computed and involved into selecting the best technique.
Link to the publication »Gas turbine diagnosability at varying operating points
І. Loboda, L.A. Miro Zarate. Gas turbine diagnosability at varying operating points / Aerospace Technics and Technology. Journal: National Aerospace University, Kharkov, Ukraine, 2014- Issue 2 (109), pp. 77-85.
The parametric diagnostics of gas turbine engines has been improved in the last decades due to computer technology development and better analysis methods such as artificial neural networks. It has demonstrated to be a very powerful tool providing an insight into an actual engine health condition and predicting possible future failures. On the basis of a thermodynamic model that relates monitored variables with operating conditions and fault parameters, it is possible to obtain healthy and faulted engine performances. This model allows calculating deviations between actual and baseline engine performances. Based on the deviations computed for all monitored variables, the diagnosis is made by pattern recognition techniques. These deviations include errors due to measurement uncertainty and model inadequacy. Since an engine operating point changes, the deviation errors change as well, resulting in varying diagnostic inaccuracy. In the present paper, two hypotheses on how the errors influence engine diagnosability at varying operating points are first investigated on simulated data and then verified with real information.
Full text »A flexible fault classification for gas turbine diagnosis
J. L. Perez Ruiz, I. Loboda. A flexible fault classification for gas turbine diagnosis / Aerospace Technics and Technology. Journal: National Aerospace University, Kharkov, Ukraine, 2014- Issue 6 (113), pp. 94-102.
Diagnostic algorithms that use gas path measurements and models are capable to diagnose not only different faults of the gas path itself but also malfunctions of measurement and control systems. Since the variety of gas turbine fault conditions is great, they are joined in a limited number of classes. Different principles to create these classes are known and there are many fault classifications in practice. In an investigation stage, it is difficult to predict what classification will then be applied in a real monitoring system therefore the investigators usually experiment with different fault types and fault numbers. The present paper proposes the approach that allows simple creating multiple classification variations including complex and realistic fault classes. This approach also permits easy changing between the variations and the pattern recognition techniques applied for each variation. As a result of application of each technique to each classification, probability of correct diagnostic decisions and execution time are determined. They are criteria of diagnosis efficiency. In this way, the approach allows studying the influence of fault classification on diagnosis efficiency. The paper performs such a study for a power plant for natural gas pipelines. Twelve classification variations are analyzed with the use of three recognition techniques: Multi-Layer Perceptron, Radial Basis Network and Probabilistic Neural Network. Additionally, a new boundary for fault severity is proposed and investigated by comparing three boundary options.
Full text »Computation and monitoring of the deviations of gas turbine unmeasured parameters
L. A. Miro Zarate and I. Loboda, Computation and monitoring of the deviations of gas turbine unmeasured parameters, ASME Turbo Expo 2015: International Technical Congress “Power for Land Sea & Air”, Montreal, Canada, June 15-19, 2015, 10p., ASME Paper No. GT2015-43862.
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.
Link to the publication »Improved turbine blade lifetime prediction
C. Maravilla Herrera, S. Yepifanov, I. Loboda, Improved turbine blade lifetime prediction, ASME Turbo Expo 2015: International Technical Congress “Power for Land Sea & Air”, Montreal, Canada, June 15-19, 2015, 13p., ASME Paper No. GT2015-43046.
Algorithms for predicting the remaining lifetime of an engine play an important role in gas turbine monitoring systems.
This paper addresses the improvement of models to determine the thermal boundary conditions that are necessary to calculate engine lifetime in critical hot components. Two methods for model development are compared. The first method uses physics-based models. The second method formulates the models based on a similarity concept.
The object of analysis is a cooled blade of a high-pressure turbine. Two unmeasured thermal boundary conditions are considered: the heating temperature and the heat transfer coefficient.
Instrumental and truncation errors are estimated for each model and 10 faulty conditions are considered to take into account the existing engine-to-engine differences and performance deterioration.
The blade temperature and the thermal stress at the critical points are calculated using the results obtained by the developed models as boundary conditions.
The results of the comparison show that the physics-based models are more robust to power plant faults. The best models for the heating temperature and the heat transfer coefficient were chosen. It is shown that the accuracy of the heating temperature model is more important for reliable lifetime prediction.
Modeling the GTE under its dynamic heating conditions
Sergiy Yepifanov, Roman Zelenskyi, Igor Loboda. Modeling the GTE under its dynamic heating conditions, Journal of Engineering for Gas Turbines and Power - Transactions of ASME, USA, March 2015, Vol. 137, Issue 3, paper 031506, pp. 1-10.
A modern gas turbine engine (GTE) is a complex non-linear dynamic system with the mutual effect of gas-dynamic and thermal processes in its components. The engine development requires the precise real-time simulation of all main operating modes.
One of the most complex operating modes for modeling is “cold stabilization”, which is the rotors acceleration without completely heated up the turbine elements. The dynamic heating problem is a topical practical issue. Solving the problem requires coordinating a gas-path model with heat and stress models, which is also a significant scientific problem.
The phenomenon of interest is the radial clearances change during engines operation and its influence on engines static and dynamic performances. To consider the clearance change, it is necessary to synthesize the quick proceeding stress-state models (QPSSM) of a rotor and a casing for the initial temperature and dynamic heating.
The unique feature of the QPSSM of GTEs is separate equation sets, which allow the heat exchange between structure elements and the gas (air) and the displacements of the turbine rotor and the casing. This ability appears as a result of determining the effect of each factor on different structural elements of the engine. The presented method significantly simplifies the model identification, which can be performed based on a precise calculation of the unsteady temperature fields of the structural elements and the variation of the radial clearance.
Thus, the present paper addresses a new method to model the engine dynamics considering its heating up. The method is based on the integration of three models: the gas-path dynamics model, the clearance dynamics model and the model of the clearance effect on the efficiency. The paper also comprises the program implementation of the models. The method was tested by applying to a particular turbofan engine.
Link to the publication »Gas turbine fault diagnosis using probabilistic neural networks
Loboda I., Olivares Robles M.A. Gas turbine fault diagnosis using probabilistic neural networks / International Journal of Turbo & Jet Engines, Walter de Gruyter GmbH und Co. KG, Germany, May 2015, Vol.32, Issue 2, pp.175-192.
Efficiency of gas turbine monitoring systems primarily depends on the accuracy of employed algorithms, in particular, pattern classification techniques for diagnosing gas path faults. In recent investigations many techniques have been applied to classify gas path faults, but recommendations for selecting the best technique for real monitoring systems are still insufficient and often contradictory.
In our previous work, three classification techniques were compared under different conditions of gas turbine diagnosis. The comparative analysis has shown that all these techniques yield practically the same accuracy for each comparison case.
The present contribution considers a new classification technique, Probabilistic Neural Network (PNN), and we compare it with the techniques previously examined. The results for all comparison cases show that the PNN is not inferior to the other techniques. We recommend choosing the PNN for real monitoring systems because it has an important advantage of providing confidence estimation for every diagnostic decision made.
Link to the publication »Alternative method of simulating sub-idle engine operation for control system synthesis
Sergii Sukhovii, Feliks Sirenko, Sergiy Yepifanov, Igor Loboda. Alternative method of simulating sub-idle engine operation for control system synthesis/ International Journal of Turbo & Jet Engines, Walter de Gruyter GmbH und Co. KG, Germany, May 2015, 9p. (published online 2015).
The steady-state and transient engine performances in control systems are usually evaluated by applying thermodynamic engine models. Most models operate between the idle and maximum power points, only recently, they sometimes address a sub-idle operating range.
The lack of information about the component maps at the sub-idle modes presents a challenging problem. A common method to cope with the problem is to extrapolate the component performances to the sub-idle range. Precise extrapolation is also a challenge. As a rule, many scientists concern only particular aspects of the problem such as the lighting combustion chamber or the turbine operation under the turned-off conditions of the combustion chamber. However, there are no reports about a model that considers all of these aspects and simulates the engine starting.
The proposed paper addresses a new method to simulate the starting. The method substitutes the non-linear thermodynamic model with a linear dynamic model, which is supplemented with a simplified static model. The latter model is the set of direct relations between parameters that are used in the control algorithms instead of commonly used component performances. Specifically, this model consists of simplified relations between the gas path parameters and the corrected rotational speed.
ASME Paper No. GT2014-25960
Link to the publication »Gas turbine fault recognition trustworthiness
Igor Loboda, Sergiy Yepifanov. Gas turbine fault recognition trustworthiness / Científica, ESIME-IPN, Mexico, Vol. 10, Num. 2, 2006, pp. 65-74.
This paper examines three methods of gas turbine parametric diagnosing. The functioning methods are simulated in the identical conditions of gradually developing faults and random measurement errors. The objectives are to tune the methods, to compare them, and to choose the best one on basis of probabilistic criteria of class correct and incorrect recognition. So, main focus of the paper is a recognition trustworthiness problem. A previous research work in this direction is united with new results and they all together are presented in more systematic form as a common approach. Besides the method comparison and selection, other ways to enhance the trustworthiness are described and the perspectives to realize the methods in real condition monitoring systems are analyzed.
Full text »A universal fault classification for gas turbine diagnosis under variable operating conditions
Loboda I., Feldshteyn Y. A universal fault classification for gas turbine diagnosis under variable operating conditions / International Journal of Turbo & Jet Engines, Freund Publishing House Ltd., Israel, 2007, Vol.24, Issue 1, рp. 11-27.
Stationary gas turbines operate continuously over a long period of time. In the process their operating conditions (control conditions and ambient conditions) vary considerably. The fact that gas turbine health monitoring must be uninterrupted creates the need for methods operating under any conditions. This article introduces a universal fault classification for gas turbines suitable for diagnosing at variable operating conditions. The concept of such a classification is thoroughly examined for a stationary power plant operating at steady states and transients.
The gas path fault classes are simulated by using non-linear static and dynamic power plant models. Each class is represented by a sample of measured values (patterns) allowing for measurement errors. These samples are fed to a neural network used later on to make a diagnosis. The trained neural network is then subjected to a statistical test that permits us to calculate the probabilities of a correct diagnosis. Based on these probabilities, the suggested classification is compared to a conventional approach formed at one fixed operating mode. This comparison is drawn under a variety of diagnostic conditions. The results go to show that the decrease in diagnosis reliability when switching to the universal classification is relatively low. On the other hand, it offers continuous gas turbine monitoring and substantially streamlines the diagnostic algorithms employed.
Link to the publication »A generalized fault classification for gas turbine diagnostics on steady states and transients
Loboda I., Yepifanov S., Feldshteyn Y. A generalized fault classification for gas turbine diagnostics on steady states and transients / Journal of Engineering for Gas Turbines and Power - Transactions of ASME, USA, October 2007, Vol. 129, Issue 4, pp. 977-985.
Gas turbine diagnostic techniques are often based on the recognition methods using the deviations between actual and expected thermodynamic performances. The problem is that the deviations generally depend on current operational conditions. However, our studies show that such a dependency can be low. In this paper, we propose a generalized fault classification that is independent of the operational conditions. To prove this idea, the probabilities of true diagnosis were computed and compared for two cases: the proposed classification and the conventional one based on a fixed operating point. The probabilities were calculated through a stochastic modeling of the diagnostic process. In this process, a thermodynamic model generates deviations that are induced by the faults, and an artificial neural network recognizes these faults. The proposed classification principle has been implemented for both steady state and transient operation of the analyzed gas turbine. The results show that the adoption of the generalized classification hardly affects diagnosis trustworthiness and the classification can be proposed for practical realization.
Link to the publication »Gas turbine diagnostics under variable operating conditions
Loboda I., Feldshteyn Y., Yepifanov S. Gas turbine diagnostics under variable operating conditions/ International Journal of Turbo & Jet Engines, Freund Publishing House Ltd., Israel, 2007, Vol.24, Issue 3-4, рp. 231-244.
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.
Link to the publication »ASME Paper No. GT2007-28085
Link to the publication »An integrated approach to gas turbine monitoring and diagnostics
Loboda I., Yepifanov S., Feldshteyn Y. An integrated approach to gas turbine monitoring and diagnostics/ International Journal of Turbo & Jet Engines, Freund Publishing House Ltd., Israel, 2009, Vol.26, Issue 2, рp. 111-126.
This paper presents an investigation of a conventional gas turbine diagnostic process and its generalization. A usual sequence of diagnostic actions consists of two stages: monitoring (fault detection) followed by diagnosis (fault identification). Such an approach neither implies fault identification nor uses the information about incipient faults unless the engine is recognized as faulty. In previous investigations we addressed diagnostics problems without examining their relation to the monitoring process. Fault classes were given by samples of patterns generated by a gas turbine performance model at engine’s steady state operation conditions. This fault simulation took into account faults of varying severity including incipient ones. A diagnostic algorithm was proposed that employed artificial neural networks to identify an actual fault. In the present paper we consider the monitoring and diagnosis as joint processes extending our previous approach to both of them. It is proposed to form two classes for the monitoring using the above-mentioned classes constructed for the diagnosis. A two-shaft industrial gas turbine has been chosen to test the proposed integrated approach to monitoring and diagnosis. A general recommendation following from the presented investigation is to identify faults simultaneously with fault detection. This permits accumulating preliminary diagnoses before the engine faulty condition is detected and a rapid final diagnosis after the fault detection.
Link to the publication »ASME Paper No. GT2008-51449.
Link to the publication »Diagnostic analysis of maintenance data of a gas turbine for driving an electric generator
Loboda I., Yepifanov S., Feldshteyn Y. Diagnostic analysis of maintenance data of a gas turbine for driving an electric generator/ International Journal of Turbo & Jet Engines, Freund Publishing House Ltd., Israel, 2009, Vol.26, Issue 4, рp. 235-251.
Monitoring algorithms analyzing measured gas path variables provide invaluable insight into gas turbine operating health. Some useful information about a gas turbine and its measurement system can be obtained from a direct analysis of raw measurements. To draw more comprehensive diagnostic information, deviations are usually calculated as discrepancies between the measured and baseline values of monitored variables. The deviations can serve as good indicators of different engine degradation mechanisms. However, there are many negative factors that tend to mask degradation effects.
For a long period of time we have analyzed quality of gas path measurement data and a deviation accuracy problem of a gas turbine power plant driving a natural gas pipeline compressor. Possible error sources were examined and some methods were proposed to improve the accuracy of deviation calculations. This paper looks at maintenance data of another object, the General Electric LM2500 gas turbine used as a drive of an electric generator. The data cover prolonged periods of axial compressor fouling with washings between them, and provide valuable information for a deviation examination. In order to reduce deviation errors, the paper considers different cases of the abnormal functioning of the sensors and baseline model inadequacy and proposes measures to avoid them. As a result of these and previous efforts, the deviations have become good fouling indicators. They are capable to quantify the increase of exhaust gas temperature (EGT) and, consequently, to improve planning axial compressor washings.
Link to the publication »ASME Paper No. GT2009-60176
Link to the publication »Gas turbine condition monitoring and diagnostics
Igor Loboda (2010). Gas turbine condition monitoring and diagnostics, Gas Turbines, Gurrappa Injeti (Ed.), ISBN: 978-953-307-146-6, Sciyo, pp. 119-144.
Link to the publication »A mixed data-driven and model based fault classification for gas turbine diagnosis
Loboda I., Yepifanov S. A mixed data-driven and model based fault classification for gas turbine diagnosis / International Journal of Turbo & Jet Engines, Freund Publishing House Ltd., Israel, 2010, Vol.27, Issue 3-4, pp. 251-264.
In modern gas turbine health monitoring systems, the diagnostic algorithms based on gas path analysis may be considered as principal. They analyze gas path measured variables and are capable of identifying different faults and degradation mechanisms of gas turbine components (e.g. compressor, turbine, and combustor) as well as malfunctions of the measurement system itself. Gas path mathematical models are widely used in building fault classification required for diagnostics because faults rarely occur during field operation. In that case, model errors are transmitted to the model-based classification, which poses the problem of rendering the description of some classes more accurate using real data.
This paper looks into the possibility of creating a mixed fault classification that incorporates both model-based and data-driven fault classes. Such a classification will combine a profound common diagnosis with a higher diagnostic accuracy for the data-driven classes.
A gas turbine power plant for natural gas pumping has been chosen as a test case. Its real data with cycles of compressor fouling were used to form a data-driven class of the fouling. Preliminary qualitative analysis showed that these data allow creating a representative class of the fouling and that this class will be compatible with simulated fault classes.
A diagnostic algorithm was created based on the proposed classification (real class of compressor fouling and simulated fault classes for other components) and artificial neural networks. The algorithm was subjected to statistical testing. As a result, probabilities of a correct diagnosis were determined. Different variations of the classification were considered and compared using these probabilities as criteria. The performed analysis has revealed no limitations for realizing a principle of the mixed classification in real monitoring systems.
Link to the publication »ASME Paper No. GT2010-23075
Link to the publication »Polynomials and neural networks for gas turbine monitoring: a comparative study
Loboda I., Feldshteyn Ya. Polynomials and neural networks for gas turbine monitoring: a comparative study / International Journal of Turbo & Jet Engines, Walter de Gruyter GmbH und Co. KG, Germany, 2011, Vol.28, Issue 3, pp. 227-236.
Gas turbine health monitoring includes the common stages of problem detection, fault identification, and prognostics. To extract useful diagnostic information from raw recorded data, these stages require a preliminary operation of computing differences between measurements and an engine baseline, which is a function of engine operating conditions. These deviations of measured values from the baseline data can be good indicators of engine health. However, their quality and the success of all diagnostic stages strongly depend on the adequacy of the baseline model employed and, in particular, on the mathematical techniques applied to create it.
To create a baseline model, we have applied polynomials and the least squares method for computing the coefficients over a long period of time. Methods were proposed to enhance such a polynomial-based model. The resulting accuracy was sufficient for reliable monitoring of gas turbine deterioration effects. The polynomials previously investigated thus far are used in the present study as a standard for evaluating artificial neural networks, a very popular technique in gas turbine diagnostics. The focus of this comparative study is to verify whether the use of networks results in a better description of the engine baseline.
Extensive field data for two different industrial gas turbines were used to compare these two techniques under various conditions. The deviations were computed for all available data, and the quality of the resulting deviation plots was compared visually. The mean error of the baseline model was used as an additional criterion for comparing the techniques.
To find the best network configurations, many network variations were realised and compared with the polynomials. Although the neural networks studied were found to be close to the polynomials in accuracy, they did not exceed the polynomials in any variation. In this way, it seems that polynomials can be successfully used for engine monitoring, at least for the gas turbines analysed herein.
Link to the publication »(ASME paper GT2010-23749)
Link to the publication »Gas turbine diagnostics
Igor Loboda (2012). Gas turbine diagnostics, Efficiency, Performance and Robustness of Gas Turbines, Konstantin Volkov (Editor.), ISBN 979-953-307-816-7, InTech, pp.191-212,
Link to the publication »Neural networks for gas turbine fault identification: multilayer perceptron or radial basis network
Loboda I., Feldshteyn Ya, Ponomaryov V. Neural networks for gas turbine fault identification: multilayer perceptron or radial basis network / International Journal of Turbo & Jet Engines, Walter de Gruyter GmbH und Co. KG, Germany, 2012, Vol.29, Issue 1, pp. 37-48.
Efficiency of gas turbine condition monitoring systems depends on quality of diagnostic analysis at all its stages such as feature extraction (from raw input data), fault detection, fault identification, and prognosis. Fault identification algorithms based on the gas path analysis may be considered as an important and sophisticated component of these systems. These algorithms widely use pattern recognition techniques, mostly different artificial neural networks. In order to choose the best technique, the present paper compares two network types: a multilayer perceptron and a radial basis network. The first network is being commonly applied to recognize gas turbine faults. However, some studies note high recognition capabilities of the second network.
For the purpose of the comparison, both networks were included into a special testing procedure that computes for each network the true positive rate that is the probability of a correct diagnosis. Networks were first tuned and then compared using this criterion. Same procedure input data were fed to both networks during the comparison. However, to draw firm conclusions on the networks’ applicability, comparative calculations were repeated with different variations of these data. In particular, two engines that differ in an application and gas path structure were chosen as a test case. By way of summing up comparison results, the conclusion is that the radial basis network is a little more accurate than the perceptron, however the former needs much more available computer memory and computation time.
Link to the publication »ASME Paper No. GT2011-46752
Link to the publication »Probabilistic Neural Networks For Gas Turbine Fault Recognition
І. Loboda, E. Rios Urban, E. Sanchez Cruces. Probabilistic neural networks for gas turbine fault recognition / Aerospace Technics and Technology. Journal: National Aerospace University, Kharkov, Ukraine, 2012- Issue 6(89), 6 p., ISSN 1727-7337.
Fault identification algorithms based on measured gas path variables constitute an important component of a gas turbine engine condition monitoring system. In addition to gas path faults diagnosis, these algorithms are capable to identify malfunctions of sensors and an engine control system. The fault identification algorithms widely use pattern recognition techniques, in particular, different artificial neural networks. Since monitoring system efficiency depends on accuracy of all systemтs components, the most exact mathematical technique should be chosen for every component. To recognize gas turbine faults, a specific network type, multilayer perceptron (MLP), is mostly applied. However, other network type, probabilistic neural network (PNN), can be applied as well. It uses a probabilistic measure to recognize the faults. In the present paper, the PNN is firstly tailored to a gas turbine diagnosis application and then compared with the MLP. The comparison has shown that both networks yield practically equal accuracy. The PNN is recommended for real gas turbine monitoring systems because, in addition to a diagnostic decision, this network provides confidence estimation for this decision.
Full text »A more realistic presentation of measurement deviation errors in gas turbine diagnostic algorithms
І. Loboda. A more realistic presentation of measurement deviation errors in gas turbine diagnostic algorithms / Aerospace Technics and Technology. Journal: National Aerospace University, Kharkov, Ukraine, 2011- Issue 5(82), pp.68-77., ISSN 1727-7337.
Gas path fault localization algorithms based on the pattern recognition theory are an important component of gas turbine monitoring systems. To simulate random measurement errors (noise) in description of fault classes, these algorithms usually involve theoretical random number distributions, like the Gaussian probability density function. A level of the simulated noise is determined on the basis of known information on typical maximum errors of different gas path sensors. However, not measurements themselves but their deviations from an engine baseline are input parameters for diagnostic algorithms. These deviations computed for real data have other error components in addition to simulated measurement inaccuracy. In this way, simulated and real deviation errors differ by an amplitude and distribution. Consequently, with such simulation, the performance of a diagnostic algorithm is poorly estimated, and therefore, the conclusion on algorithm efficiency may be wrong. To understand better noise peculiarities, plots of deviations of real measurements are tracked in the present paper. Additionally, possible deviation errors are surely analyzed analytically. To make noise presentation more realistic, it is proposed to extract random errors from real deviations and to integrate these errors in fault description. Finally, the effect of the new noise representation mode on gas turbine diagnosis reliability is estimated.
Full text »Radial Basis Functions For Gas Turbine Fault Recognition
I.І. Loboda, L.A. Miro Zarate, A.E. Leal Bolanos. Radial basis functions for gas turbine fault recognition / Aerospace Technics and Technology. Journal: National Aerospace University, Kharkov, Ukraine, 2010- Issue 10(77), pp.182-186, ISSN 1727-7337.
Artificial neural networks present a fast growing computing technique for many fields of applications including gas turbine diagnostics. This paper examines the network based on radial basis functions (radial basis network) and applied to recognise gas path faults. To assess diagnosis efficiency of the radial basis network (RBNs), it is compared with a multilayer perceptron. During the comparison, input data are the same for the both networks;however comparative calculations are repeated for different variations of these data allowing to draw more general conclusions on the RBN applicability. The objectives are to tune the networks, to compare them, and to assess efficiency of the RBN on basis of a probabilistic criterion of correct fault recognition. The comparison results show that the RBN is a perspective technique for gas turbine diagnosis.
Full text »Diagnostic analysis of gas turbine hot section temperature measurements
Igor Loboda, Yakov Feldshteyn, Claudia Fernanda Villarreal González. Diagnostic analysis of gas turbine hot section temperature measurements / Aerospace Technics and Technology. Journal: National Aerospace University, Kharkov, Ukraine, 2009- Issue 6(63), pp.66-79, ISSN 1727-7337.
Temperatures measured in a hot section of gas turbines are very important for a gas path analysis. A suite of parallel thermocouples are usually installed in the same gas path station in order to compute a filtered and averaged temperature quantity for its further use in control and diagnostic systems. However, in spite of the preliminary treatment, the resulting quantity is not completely free from errors. To eliminate or reduce the errors, the present paper analyzes anomalies in the behaviour of each thermocouple of an industrial gas turbine engine. To that end, time graphs of both measured magnitudes themselves and their deviations from reference magnitudes are plotted. In order to draw sound conclusions, the analysis is conducted on a large volume of the data collected for three particular engines.
Full text »Probabilistic Neural Networks For Gas Turbine Fault Recognition
Igor Loboda, Ivan Karol Trahyn Amescua. Compressor fouling identification on gas turbine field data / Aerospace Technics and Technology. Journal: National Aerospace University, Kharkov, Ukraine, 2008- Issue 10(57), pp.192-199, ISSN 1727-7337.
Gas turbine fault isolation or identification generally uses a model-based classification of gas path faults. This classification is not too exact because of model errors. The present paper looks at the possibility to create a fault class on the basis of gas turbine real data containing cycles of a compressor fouling and washing. The concerned data-driven fouling class formation is realized in the space of deviations of measured gas path quantities. Analyzing deviation plots for different fouling cycles, we have confirmed identifiability of the fouling. In order to draw sound conclusions, the analysis was conducted for two gas turbines of different application.
Full text »Neural Networks-based Gas Turbine Fault Recognition
Loboda I., Gutierrez Flores V. H., Cruz Irrison M. Neural networks-based gas turbine fault recognition / Aerospace Technics and Technology. Journal: National Aerospace University, Kharkov, Ukraine, 2007- Issue 9(45), pp. 196 – 203, ISSN 1727-7337.
The main focus of this paper is reliable fault recognition for gas turbines. Gas path models are employed to describe different faults of variable severity. To recognize them, two methods are used and examined in the paper. The first method is based on the Bayesian recognition while the second applies neural networks. The recognition process for the both methods is simulated numerously under the conditions of random measurement errors, and diagnosis errors are fixed. The objectives are to verify the methods statistically, adjust them, and compare the networks' recognition errors with the Bayesian recognition ones. To make the accuracy analysis more general, the paper compares the methods for two fault classification variants and different gas turbine operating conditions.
Full text »Gas Turbine Diagnostic Model Identification On Maintenance Data Of Great Volume
Loboda I. Gas turbine diagnostic model identification on maintenance data of great volume / Aerospace Technics and Technology. Journal: National Aerospace University, Kharkov, Ukraine, 2007- Issue 10(46), pp. 198 – 204, ISSN 1727-7337.
This paper deals with an identification procedure of gas turbine nonlinear models for monitoring and diagnostic systems. Introduction of a special time variable into a conventional thermodynamic model helps to create a model of the engine with a variable deterioration level. To identify this model, registration data of great volume and different gas turbine deterioration severity can be attracted. This ensures high accuracy of the identified model as well as quality of a baseline function that can simply be extracted from the model.
Full text »Reliability Enhancement Of Gas Turbine Fault Identification
Loboda I. Reliability enhancement of gas turbine fault identification / Aerospace Technics and Technology. Journal: National Aerospace University, Kharkov, Ukraine, 2006- Issue 9(35), pp. 140 – 150, ISSN 1727-7337.
Main focus of this paper is a reliable fault identification of gas turbines. The paper examines the methods of gas turbine parametric diagnosing. The nonlinear thermodynamic model describes different gradually developing faults. To identify them, the method operation is simulated in the conditions of random measurement errors, the correct and incorrect diagnostic decisions are fixed, and corresponding averaged probabilities are computed. The objectives are to verify the methods statistically, adjust them, and choose the best one which ensures the higher probability of fault correct identification. Besides the method comparison, the paper also considers a problem of classifying the various gas turbine faults. Different classification variants are presented to make more general the analysis of diagnosing process. A generalized fault classification which unites the fault descriptions from different operational regimes is also proposed and analyzed. It conserves a trustworthiness level of the previous regime dependent classification and makes the diagnosing be much more universal.
Full text »Loboda I., Nakano Miyatake M., Goryachiy A., Gutiérrez Mojica E.M., González Aguilar J.E. Gas turbine fault recognition by artificial neural networks, Memorias del 4to Congreso Internacional de Ingenieria Electromecanica y de Sistemas, ESIME, IPN, Mexico, 14-18 noviembre de 2005, ISBN: 970-36-0292-4, 6p.
This paper examines an application of artificial neural networks to gas turbine engine diagnosing based on gaspath measured variables. Main focus of the paper is to choose and tune neural networks for diagnostic purposes using trustworthiness indicators. Fault classes are made up from residuals generated by the nonlinear thermodynamic gas turbine model for different gradually developing faults of gas turbine components. The residuals are computed as differences between the measured variables of a faulted engine and a healthy one and also take in account random measurement errors. The trustworthiness indicators present averaged probabilities of gas turbine fault correct recognition. To calculate them, the operation of the neural networks-based diagnostic technique is simulated numerously on different input data, the correct diagnostic decisions are fixed, and corresponding averaged probabilities are computed then. We analysed many variants of network structures and teaching methods and compared them by the trustworthiness indicators. As a result, the optimal network structure and learning method have been chosen. Comparing the artificial neural networks technique with other diagnostic methods, it can be stated that networks application makes gas turbine diagnosis more reliable.
Full text »Verification of a Gas Turbine Model
Loboda I., Zelenskiy R., Nerubasskiy V., López y Rodríguez A.R. Verification of a gas turbine model regularizing identification procedure on simulated and real data, Memorias del 4to Congreso Internacional de Ingenieria Electromecanica y de Sistemas, ESIME, IPN, Mexico, 14-18 noviembre de 2005, ISBN: 970-36-0292-4, 6p.
Identification of a gas turbine thermodynamic model may be interpreted as an inverse problem and therefore is potentially unstable. Regularization is a common principle to make a solution of any mathematical problem more robust to possible input data perturbances. In this paper with a purpose of diagnostic application, we propose a regularizing algorithm of gas turbine model identification. To verify the developed software, choose a proper regularization degree and determine averaged accuracy indicators, the algorithm is put to the statistical testing. The results obtained on both simulated and real input data show that the algorithm has certain advantages and can be constructed and work successfully in gas turbine operating health monitoring systems.
Full text »Deviation Problem In Gas Turbine Health Monitoring
I. Loboda, S. Yepifanov, Y. Feldshteyn. Deviation problem in gas turbine health monitoring / IASTED International Conference on Power and Energy Systems (PES-2004), Clearwater Beach, Florida, USA, November 28 – December 1, 2004, pp. 335-340.
This paper looks at the deviations forming as differences between actual measurements and a normal state function for further application in gas turbine monitoring, diagnostics, and forecasting. The focus of the paper is possible greatest reducing the deviation random errors in order to observe more clearly the trends and to detect corresponding gas turbine faults. Possible error sources are examined and some methods are proposed to enhance the normal function and the deviations. Real maintenance data and gas turbine nonlinear thermodynamic model are applied to realize and verify these methods.
Full text »Trustworthiness Problem Of Gas Turbine Parametric Diagnosing
Loboda I. Trustworthiness problem of gas turbine parametric diagnosing, 5th IFAC Symposium on Fault detection, Supervision and Safety of Technical Processes “Safeprocess-2003”, Washington, D.C., USA, June 9-11, 2003, 6p.
In the paper, different gas turbine parametric diagnosing methods are analyzed on the base of trustworthiness criteria. The nonlinear gaspath thermodynamic models, random number generators, and real registration data are used for methods adjustment and verification. The achieved trustworthiness level is sufficient to recommend the examined methods for realization in automated diagnosing systems.
Full text »Statistical Testing Of Dynamic Model Identification Procedure For Gas Turbine Diagnosis
S. Yepifanov, I. Loboda, Y. Feldshteyn. Statistical testing of dynamic model identification procedure for gas turbine diagnosis/ Aerospace Technics and Technology. Journal: National Aerospace University, Kharkov, Ukraine, 2003- Issue 7(42), pp. 122 – 128, ISSN 1727-7337.
For a gas turbine that is considered as a complex and expensive system, condition monitoring technologies have the potential to save millions of dollars per year [1], through lowering fuel consumption, reducing catastrophic failures, decreasing mean time to repair, and optimizing maintenance planning. This will significantly reduce life cycle cost and improve competitive position of companies that maintain engines equipped with condition (health) monitoring systems.
Full text »Dynamic Model Identification Procedure
E.L.Santiago, I.Loboda. Dynamic model identification procedure for gas turbine diagnosis, Memorias del 7 Congreso Nacional de Ingenieria Electromecanica, SEPI-ESIME, Mexico, 3-7 noviembre 2003, 4p.
Modern gas turbine engines represent powerful and efficient sources of mechanical energy for numerous industry branches and transport objects. For a gas turbine that is considered as a complex and expensive system, condition monitoring technologies have the potential to save millions of dollars per year [1], through lowering fuel consumption, reducing catastrophic failures, decreasing mean time to repair, and optimizing maintenance planning. This will significantly reduce life cycle cost and improve competitive position of companies that maintain engines equipped with condition (health) monitoring systems.
Full text »Development Of Normal State Model
Julio César Hernández González, Igor Loboda, Carlos de la Cruz Alejo. Development of normal state model for gas turbine performance monitoring / Memorias del 3er Congreso Internacional de Ingenieria Electromecanica y de Sistemas, ESIME-Zacatenco, Mexico, 25-29 noviembre 2002, pp. 243 – 246, ISBN: 970-18-9516-9.
For a determination of gas turbine technical state many diagnosing techniques analyze in real time the deviations of measured parameters from a normal state model. Above performance degradation a lot of factors influence deeply on these deviations too and diagnosing success depends on model perfection. This paper looks at the process of creation and adjustment of such gaspath parameter model on the base of application of real maintenance data and nonlinear thermodynamic model. The main focus of the work was to find the best model variant for gas turbine monitoring system.
Full text »Nonlinear Dynamic Model Identification Of Gas Turbine Engine.
Igor Loboda, Julio Cesar Hernandez Gonzalez. Nonlinear Dynamic Model Identification of Gas Turbine Engine / Aerospace Technics and Technology. Journal: National Aerospace University, Kharkov, Ukraine, 2002- Issue 31, pp. 209 - 211, ISBN 966-7427-08-0, 966-7458-58-X.
Design of gas turbine engine itself and its diagnosing systems is deeply related with calculations on the base of gaspath mathematical models of different complexity, and the identification procedure represents not only an effective instrument to raise an accuracy of the models and related calculations but also an important component of diagnosing algorithms. Practical application of such instruments as nonlinear mathematical model and its identification procedure becomes a common practice [1,2]. Last computer progress stimulates an elaboration and application of more sophisticated instruments.
Full text »Identification Procedure Development of Gas Turbine Nonlinear Dynamic Model.
Dr. Igor Loboda IPN, ESIME-Culhuacan, Secciуn de Estudios de Posgrado e Investigaciуn, Av. Santa Ana 1000, Col. San Francisco Culhuacan, Mйxico, D.F., 04430 MEXICO E-mail: loboda@calmecac.esimecu.ipn.mx September 13, 2002
This paper describes an algorithm of identification procedure of gas turbine nonlinear dynamic model and a software structure developed. The procedure was examined on simulated and real data. The results confirm that the identification pro- cedure is perspective for an application in gas turbine control and diagnosing sys- tems.
Full text »Algorithm Of Optimal Load Distribution Between Gas Pump Units Of Compressor Shop.
S.Yepifanov, I.Loboda. Algorithm of optimal load distribution between gas pump units of compressor shop/ Aerospace Technics and Technology. Journal: National Aerospace University, Kharkov, Ukraine, 2001- Issue 23, pp. 175-176, ISBN 966-7839-31-1.
The function of a compressor shop is to restore the gas pressure reduction caused by frictional pressure losses in a pipeline. Shop compressors are driven by gas turbines which all together are known as gas pump units. Gas turbines use natural gas as fuel and minimizing this fuel usage is a major objective in the control of the compressor shop.
Full text »Problems Of Gas Turbine Diagnostic Model Identification On Maintenance Data.
I.Loboda, E.L.Santiago. Problems of gas turbine diagnostic model identification on maintenance data / Memorias del 6 Congreso Nacional de Ingenieria Electromecanica, ESIME-Zacatenco, Mexico, 27-30 noviembre 2001, pp.332-334. ISBN 970-18-7441-2.
Automatic diagnosing system is an integral part of modern gas turbine power plants (gas turbines), and is applied not only for maintenance needs but for aims of gas turbine development ant tests too. The algorithms of fault detection based on treatment of gas path measured parameters (gas pressures and temperatures, rotor rotation speeds, fuel consumption, …) are considered as principal and most complex. Above the gas path the control system and measurement system may be diagnosed too on these parameters [1,2] without gas turbine shut down and disassembling.
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