| 1. |
- Aslanidou, Ioanna, et al.
(författare)
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Development of web-based short courses on control, diagnostics, and instrumentation
- 2020
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Ingår i: Proceedings of the ASME Turbo Expo 2020, Sep 21-25. - 9780791884157
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Konferensbidrag (refereegranskat)abstract
- As a consequence of globalization and advances in digital tools, synchronous or asynchronous distance courses are becoming an integral part of universities’ educational offers. The design of an online course introduces more challenges compared to a traditional on campus course with face to face lectures. This is true especially for engineering subjects where problem or project-based courses may be preferred to stimulate critical thinking and engage the learners with real-life problems. However, realizing this with distance learning implies that a similar study pace should be kept by the learners involved. This may not be easy, since individual pace is often a motivation for choosing a distance course. Student engagement in group projects, collaborations, and the proper design of examination tasks are only some of the challenges in designing a distance course for an engineering program. A series of web-based courses on measurement techniques, control, and diagnostics were developed and delivered to groups of learners. Each course comprised short modules covering key points of the subject and aimed at getting learners to understand both the fundamental concepts that they do not typically learn or understand in the respective base courses and to build on that knowledge to reach a more advanced cognitive level. The experience obtained in the courses on what strategies worked better or worse for the learners is presented in this paper. A comparison between the courses provides an interesting outlook on how the learners reacted to slightly different requirements and incentives in each course. The results from the evaluation of the courses are also used as a base for discussion.The background and availability of the learners is closely linked to how a course should be designed to optimally fit the learning group, without compromising on the achievement of the learning outcomes. This series of courses is a good example of continuous professional development courses in the field of control, diagnostics, and instrumentation (CDI), and brings with it a number of challenges and opportunities for the development of online courses.
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| 2. |
- Baheta, Aklilu, et al.
(författare)
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CFD Analysis of Fouling Effects on Aerodynamics Performance of Turbine Blades
- 2018
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Ingår i: Rotating Machineries. - Singapore : Springer. - 9789811323560 - 9789811323577 ; , s. 73-84
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Bokkapitel (refereegranskat)abstract
- Fouling on gas turbine blades is detrimental to process operation as it may, over a period of time, reduce the blade efficiency and consequently the turbine’s efficiency. With the limitation of today’s technology, experimental study or real-life observation of fouling in a gas turbine is beyond imagination of maintenance engineers. Hence, the effect of fouling cannot be fully quantified for the engineers to come out with mitigation or intervention plans. Nevertheless, computational fluid dynamics (CFD) may provide a good simulation to understand the phenomena. In this chapter, recent effort involving CFD study on the influence of fouling on gas turbine performance is presented. Firstly, the nature of fouling on the gas turbine and the general consequences are discussed. This is followed by an elaboration on how CFD study has been conducted by the authors. Finally, the findings from the study are discussed.
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| 3. |
- Baheta, Aklilu, et al.
(författare)
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DEVELOPMENT AND VALIDATION OF A TWIN SHAFT INDUSTRIAL GAS TURBINE PERFORMANCE MODEL
- 2016
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Ingår i: Journal of Engineering and Applied Sciences. - 1819-6608. ; 22:11, s. 13365-13371
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Tidskriftsartikel (refereegranskat)abstract
- Gas turbine performance is very responsive to ambient and operational conditions. If the engine is not operating atits optimum conditions, there will be high energy consumption and environmental pollution. Hence, a precise simulationmodel of a gas turbine is needed for performance evaluation and fault detection and diagnostics. This paper presents a twinshaft industrial gas turbine modeling and validation. To develop the simulation model component maps are important,however they are property of the manufacturers and classified documents. In this case, known the compressor pressureratio, speed, and flow rate, the missing design parameters, namely turbines inlet temperatures and pressure ratios werepredicted using GasTurb simulation software. Once the design parameters are developed, the nearest compressor andturbine maps were selected from GasTurb map collection. Beta lines were introduced on each map so that the exactcorresponding value can be picked for a given two parameters of a given map. After the completion of components model,a simulation model was developed in Matlab environment. The equations governing the operation of individual componentwere solved using iteration method. The simulation model has modular nature; it can be modified easily when a change isrequired. The parameters that the model can predict include terminal temperature and pressure, flow rate, specific fuelconsumption, thermal efficiency and heat ratio. To demonstrate the validity of the developed model, the performance ofGE LM2500 twin shaft gas turbine operating in a gas oil industry at Resak PETRONAS platform in Malaysia waspredicted and compared with operational data. The results showed that an average of 5, 3.8 and 3.7 % discrepancies forcompressor discharge temperature and pressure, and fuel flow rate, respectively. This comparison of results showed goodagreement between the measured and predicted parameters. Thus, the developed model can be helpful in performanceevaluation of twin shaft gas turbines and generation of data for training and validation of a fault detection and diagnosticmodel.
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| 4. |
- Fentaye, Amare Desalegn, et al.
(författare)
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A Review on Gas Turbine Gas-Path Diagnostics : State-of-the-Art Methods, Challenges and Opportunities
- 2019
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Ingår i: Aerospace. - Zurich, Switzerland : MDPI. - 2226-4310. ; 6:7
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Forskningsöversikt (refereegranskat)abstract
- Gas-path diagnostics is an essential part of gas turbine (GT) condition-based maintenance (CBM). There exists extensive literature on GT gas-path diagnostics and a variety of methods have been introduced. The fundamental limitations of the conventional methods such as the inability to deal with the nonlinear engine behavior, measurement uncertainty, simultaneous faults, and the limited number of sensors available remain the driving force for exploring more advanced techniques. This review aims to provide a critical survey of the existing literature produced in the area over the past few decades. In the first section, the issue of GT degradation is addressed, aiming to identify the type of physical faults that degrade a gas turbine performance, which gas-path faults contribute more significantly to the overall performance loss, and which specific components often encounter these faults. A brief overview is then given about the inconsistencies in the literature on gas-path diagnostics followed by a discussion of the various challenges against successful gas-path diagnostics and the major desirable characteristics that an advanced fault diagnostic technique should ideally possess. At this point, the available fault diagnostic methods are thoroughly reviewed, and their strengths and weaknesses summarized. Artificial intelligence (AI) based and hybrid diagnostic methods have received a great deal of attention due to their promising potentials to address the above-mentioned limitations along with providing accurate diagnostic results. Moreover, the available validation techniques that system developers used in the past to evaluate the performance of their proposed diagnostic algorithms are discussed. Finally, concluding remarks and recommendations for further investigations are provided.
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| 5. |
- Fentaye, Amare Desalegn, et al.
(författare)
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Aircraft engine performance monitoring and diagnostics based on deep convolutional neural networks
- 2021
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Ingår i: Machines. - : MDPI. - 2075-1702. ; 9:12
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Tidskriftsartikel (refereegranskat)abstract
- The rapid advancement of machine-learning techniques has played a significant role in the evolution of engine health management technology. In the last decade, deep-learning methods have received a great deal of attention in many application domains, including object recognition and computer vision. Recently, there has been a rapid rise in the use of convolutional neural networks for rotating machinery diagnostics inspired by their powerful feature learning and classification capability. However, the application in the field of gas turbine diagnostics is still limited. This paper presents a gas turbine fault detection and isolation method using modular convolutional neural networks preceded by a physics-driven performance-trend-monitoring system. The trend-monitoring system was employed to capture performance changes due to degradation, establish a new baseline when it is needed, and generatefault signatures. The fault detection and isolation system was trained to step-by-step detect and classify gas path faults to the component level using fault signatures obtained from the physics part. The performance of the method proposed was evaluated based on different fault scenarios for a three-shaft turbofan engine, under significant measurement noise to ensure model robustness. Two comparative assessments were also carried out: with a single convolutional-neural-network-architecture-based fault classification method and with a deep long short-term memory-assisted fault detection and isolation method. The results obtained revealed the performance of the proposed method to detect and isolate multiple gas path faults with over 96% accuracy. Moreover, sharing diagnostic tasks with modular architectures is seen as relevant to significantly enhance diagnostic accuracy.
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| 6. |
- Fentaye, Amare Desalegn, et al.
(författare)
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An intelligent data filtering and fault detectionmethod for gas turbine engines
- 2020
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Ingår i: MATEC Web of Conferences 314. - : EDP Sciences. - 2261-236X.
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Konferensbidrag (refereegranskat)abstract
- In a gas turbine fault diagnostics, the removal of measurementnoise and data outliers prior to the fault analysis is very essential. Theconventional filtering methods, particularly the linear ones, are notsufficiently accurate, which might possibly lead to the loss of criticallyimportant features in the fault analysis process. Conversely, the recordedaccuracies obtained from the non-linear filters are promising. Recently, thefocus has been shifted to the artificial neural network (ANN) based nonlinearfilters due to their capability of providing a robust identity map between theinput and output data, which can be efficiently exploited in the process offault diagnosis. This paper aims to present combined auto-associative neuralnetwork (AANN) and K-nearest neighbor (KNN) based noise reduction andfault detection method for a gas turbine engine application. The performanceof the developed method has been evaluated using data obtained from amodel simulation. The test results revealed that the developed hybrid methodis more effective and reliable than the conventional methods for the faultdetection of the gas turbine engine with negligible false alarms and misseddetections.
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| 7. |
- Fentaye, Amare Desalegn, et al.
(författare)
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Discrimination of rapid and gradual deterioration for an enhanced gas turbine life-cycle monitoring and diagnostics
- 2021
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Ingår i: International Journal of Prognostics and Health Management. - : Prognostics and Health Management Society. - 2153-2648. ; 12:3, s. 1-16
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Tidskriftsartikel (refereegranskat)abstract
- Advanced engine health monitoring and diagnostic systems greatly benefit users helping them avoid potentially expensive and time-consuming repairs by proactively identifying shifts in engine performance trends and proposing optimal maintenance decisions. Engine health deterioration can manifest itself in terms of rapid and gradual performance deviations. The former is due to a fault event that results in a short-term performance shift and is usually concentrated in a single component. Whereas the latter implies a gradual performance loss that develops slowly and simultaneously in all engine components over their lifetime due to wear and tear. An effective engine lifecycle monitoring and diagnostic system is therefore required to be capable of discriminating these two deterioration mechanisms followed by isolating and identifying the rapid fault accurately. In the proposed solution, this diagnostic problem is addressed through a combination of adaptive gas path analysis and artificial neural networks. The gas path analysis is applied to predict performance trends in the form of isentropic efficiency and flow capacity residuals that provide preliminary information about the deterioration type. Sets of neural network modules are trained to filter out noise in the measurements, discriminate rapid and gradual faults, and identify the nature of the root cause, in an integrated manner with the gas path analysis. The performance of the proposed integrated method has been demonstrated and validated based on performance data obtained from a three-shaft turbofan engine. The improvement achieved by the combined approach over the gas path analysis technique alone would strengthen the relevance and long-term impact of our proposed method in the gas turbine industry. © 2021, Prognostics and Health Management Society. All rights reserved.
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| 8. |
- Fentaye, Amare Desalegn, et al.
(författare)
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Effects of performance deterioration on gas path measurements in an industrial gas turbine
- 2016
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Ingår i: Journal of Engineering and Applied Sciences. - 1819-6608. ; 24:1, s. 14202-14207
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Tidskriftsartikel (refereegranskat)abstract
- Studying gas turbine degradation causes and their consequences helps to obtain profound comprehension in howperformance deterioration affects the dependent parameters and to explore relevant information about the nature of thefault signatures for fault diagnostics purpose. In this paper, the effects of compressor fouling, gas generator turbine erosion,and power turbine erosion on the engine dependent parameters were considered separately and together. In this regard,firstly, performance prediction model was developed to LM2500 engine using gas turbine simulation program. It was thenused to simulate the deterioration effects by means of artificially implanted fault case patterns. Comparison of the cleanand deteriorated measurement gives the deviation due to performance degradation. Accordingly, sensitivity order of the gaspath parameters to the corresponding performance deterioration was assessed. This helps to select the key parameters,which are crucial in the process of fault detection and isolation. The results showed that, in most of the cases, air mass flowrate, compressor delivery pressure and temperature, gas generator rotational speed, power turbine inlet pressure, andexhaust gas temperature showed significant deviations. Particularly, the compressor delivery pressure and exhaust gastemperature were the parameters highly influenced by all the fault cases. Moreover, faults that have similar impacts areidentified, in order to show the difficulty of gas turbine health assessment through direct observation to the measurementdeviations.
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| 9. |
- Fentaye, Amare Desalegn, et al.
(författare)
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Gas path fault diagnostics using a hybrid intelligent methodfor industrial gas turbine engines
- 2018
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Ingår i: Journal of the Brazilian Society of Mechanical Sciences and Engineering. - : Springer. - 1678-5878 .- 1806-3691. ; 40
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Tidskriftsartikel (refereegranskat)abstract
- There are many challenges against an accurate gas turbine fault diagnostics, such as the nonlinearity of the engine health,the measurement uncertainty, and the occurrence of simultaneous faults. The conventional methods have limitations ineffectively handling these challenges. In this paper, a hybrid intelligent technique is devised by integrating an autoassociativeneural network (AANN), nested machine learning (ML) classifiers, and a multilayer perceptron (MLP). The AANNmodule is used as a data preprocessor to reduce measurement noise and extract the important features for visualisation andfault diagnostics. The features are extracted from the bottleneck layer output values based on the concept of the nonlinearprincipal component analysis (NLPCA). The nested classifier modules are then used in such a manner that fault and no-faultconditions, component and sensor faults, and different component faults are distinguished hierarchically. As part of the classification,evaluation of the fault classification performance of five widely used ML techniques aiming to identify alternativeapproaches is undertaken. In the end, the MLP approximator is utilised to estimate the magnitude of the isolated componentfaults in terms of flow capacity and isentropic efficiency indices. The developed system was implemented to diagnose up tothree simultaneous faults in a two-shaft industrial gas turbine engine. Its robustness towards the measurement uncertaintywas also evaluated based on Gaussian noise corrupted data. The test results show the derivable benefits of integrating twoor more methods in engine diagnostics on the basis of offsetting the weakness of the one with the strength of another.
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| 10. |
- Fentaye, Amare Desalegn, et al.
(författare)
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Gas turbine gas path diagnostics: : A review
- 2016
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Ingår i: MATEC Web of Conferences 74, 00005. - : EDP Sciences. - 2261-236X.
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Konferensbidrag (refereegranskat)abstract
- In this competitive business world one way to increase profitability of a power production unit is to reduce the operation and maintenance expenses. This is possible if the gas turbine availability and reliability is improved using the appropriate maintenance action at the right time. In that case, fault diagnostics is very critical and effective and advanced methods are essential. Gas turbine diagnostics has been studied for the past six decades and several methods are introduced. This paper aims to review and summarise the published literature on gas path diagnostics, giving more emphasis to the recent developments, and identify advantages and limitations of the methods so that beginners in diagnostics can easily be introduced. Towards this end, this paper, identifies various diagnostic methods and point out their pros and cons. Finally, the paper concludes the review along with some recommended future works.
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