Diagnostic Analysis of Maintenance Data of a Gas Turbine for Driving an Electric Generator

Loboda, Igor; Yepifanov, Sergiy; Feldshteyn, Yakov

doi:10.1115/gt2009-60176

Cited by 6 publications

(4 citation statements)

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“…In addition to the case of thermocouples considered before, many other cases of abnormal sensor data behaviour have been detected and interpreted. The reader can find them in Loboda et al, 2004;Loboda, Yepifanov et al, 2009). a) Deviations dTt i for 5 thermocouple probes and a deviation dTt med of a mean EGT variable b) EGT measurement by 11 thermocouple probes: anomaly cases…”

Section: Fig 2 Gt1 Deviations Ymentioning

confidence: 99%

“…Adequacy of this model depends on a correct choice of three elements: model arguments (elements of the vector U → ), type of an approximating function, and reference set. Many variations of these factors have been proposed, realized and compared (Loboda et al, 2004;Loboda, Yepifanov et al, 2009) in order to choose the best choice of each factor. The criterion to compare the choices was an integral quality of the corresponding deviations.…”

Section: Deviations and Baseline Model Adequacymentioning

confidence: 99%

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Gas Turbine Diagnostics

Loboda¹

2012

Efficiency, Performance and Robustness of Gas Turbines

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A gas turbine engine can be considered as a very complex and expensive mechanical system; furthermore, its failure can cause catastrophic consequences. That is why it is desirable to provide the engine by an effective condition monitoring system. Such an automated system based on measured parameters performs monitoring and diagnosis of the engine without the need of its shutdown and disassembly. In order to improve gas turbine reliability and reduce maintenance costs, many advanced monitoring systems have been developed recent decades. Design and use of these systems were spurred by the progress in instrumentation, communication techniques, and computer technology. In fact, development and use of such systems has become today a standard practice for new engines.As shown in (Rao, 1996), an advanced monitoring system consists of different components intended to cover all gas turbine subsystems. A diagnostic analysis of registered gas path variables (pressures, temperatures, rotation speeds, fuel consumption, etc.) can be considered as a principal component and integral part of the system. Many different types of gas path performance degradation, such as foreign object damage, fouling, tip rubs, seal wear, and erosion, are known and can be diagnosed. Detailed descriptions of these abrupt faults and gradual deterioration mechanisms can be found, for instance, in (Rao, 1996; Meher-Homji et al., 2001). In addition to the mentioned gas path faults, the analysis of gas path variables (gas path analysis, GPA) also allows detecting sensor malfunctions and wrong operation of a control system . Moreover, this analysis allows estimating main measured engine performances like shaft power, thrust, overall engine efficiency, specific fuel consumption, and compressor surge margin.The GPA is an area of extensive studies and thousands of published works can be found in this area. Some common observations that follow from the publications and help to explain the structure of the present chapter are given below.According to known publications, a total diagnostic process usually includes a preliminary stage of feature extraction and three principal stages of monitoring (fault detection), detailed diagnosis (fault localization), and prognosis. Each stage is usually presented by specific algorithms.The feature extraction means extraction of useful diagnostic information from raw measurement data. This stage includes measurement validation and computing deviations. The deviation of a monitored variable is determined as a discrepancy between a measured value and an engine base-line model. In contrast to the monitored variables themselves that www.intechopen.com

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Section: Fig 2 Gt1 Deviations Ymentioning

confidence: 99%

Section: Deviations and Baseline Model Adequacymentioning

confidence: 99%

Gas Turbine Diagnostics

Loboda¹

2012

Efficiency, Performance and Robustness of Gas Turbines

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Section: Deviations and Sensor Malfunctionsmentioning

confidence: 99%

Efficiency, Performance and Robustness of Gas Turbines

Волков¹

2012

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London (UK). His areas of expertise cover multidisciplinary areas: from applied engineering problems related to design and optimization of energy systems to fundamental problems focused on computational fluid dynamics and mathematical modelling. He has a wide experience in design, development and optimisation of engineering systems and technological processes. Dr K Volkov works in close collaboration with industrial companies and is actively involved in bidding for funding from national and international organizations. He has more than 120 published scientific publications including books and book chapters. He is a Chartered Engineer and a member of the

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“…One of the major challenges for gas turbine users is to ensure a high level of availability and reliability during the entire operating life of the engine while maintaining efficient operation. For tackling this challenge integration of advanced diagnostic methods can be of great assistance as discussed by Volponi (Volponi, 2014) and demonstrated through applications by (Loboda et al, 2009;Verbist et al, 2013;Roumeliotis et al, 2017). Several diagnostic methods have been proposed for assessing engine health condition (e.g.…”

Section: Introductionmentioning

confidence: 99%

Application of an advanced adaptation methodology for gas turbine performance monitoring

Rompokos¹,

Aretakis²,

Roumeliotis³

et al. 2020

Proceedings of Global Power &Amp; Propulsion Society

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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.

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Diagnostic Analysis of Maintenance Data of a Gas Turbine for Driving an Electric Generator

Cited by 6 publications

References 0 publications

Gas Turbine Diagnostics

Gas Turbine Diagnostics

Efficiency, Performance and Robustness of Gas Turbines

Application of an advanced adaptation methodology for gas turbine performance monitoring

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