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Fentaye, Amare Desalegn
Publications (10 of 27) Show all publications
Hashmi, M. B., Mansouri, M., Fentaye, A. D., Ahsan, S. & Kyprianidis, K. (2024). An Artificial Neural Network-Based Fault Diagnostics Approach for Hydrogen-Fueled Micro Gas Turbines. Energies, 17(3), Article ID 719.
Open this publication in new window or tab >>An Artificial Neural Network-Based Fault Diagnostics Approach for Hydrogen-Fueled Micro Gas Turbines
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2024 (English)In: Energies, E-ISSN 1996-1073, Vol. 17, no 3, article id 719Article in journal (Refereed) Published
Abstract [en]

The utilization of hydrogen fuel in gas turbines brings significant changes to the thermophysical properties of flue gas, including higher specific heat capacities and an enhanced steam content. Therefore, hydrogen-fueled gas turbines are susceptible to health degradation in the form of steam-induced corrosion and erosion in the hot gas path. In this context, the fault diagnosis of hydrogen-fueled gas turbines becomes indispensable. To the authors' knowledge, there is a scarcity of fault diagnosis studies for retrofitted gas turbines considering hydrogen as a potential fuel. The present study, however, develops an artificial neural network (ANN)-based fault diagnosis model using the MATLAB environment. Prior to the fault detection, isolation, and identification modules, physics-based performance data of a 100 kW micro gas turbine (MGT) were synthesized using the GasTurb tool. An ANN-based classification algorithm showed a 96.2% classification accuracy for the fault detection and isolation. Moreover, the feedforward neural network-based regression algorithm showed quite good training, testing, and validation accuracies in terms of the root mean square error (RMSE). The study revealed that the presence of hydrogen-induced corrosion faults (both as a single corrosion fault or as simultaneous fouling and corrosion) led to false alarms, thereby prompting other incorrect faults during the fault detection and isolation modules. Additionally, the performance of the fault identification module for the hydrogen fuel scenario was found to be marginally lower than that of the natural gas case due to assumption of small magnitudes of faults arising from hydrogen-induced corrosion.

Place, publisher, year, edition, pages
MDPI, 2024
Keywords
hydrogen fuel, micro gas turbines, health degradation, steam-induced corrosion, fault detection, diagnostics
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-66085 (URN)10.3390/en17030719 (DOI)001160097200001 ()2-s2.0-85184656336 (Scopus ID)
Available from: 2024-02-20 Created: 2024-02-20 Last updated: 2024-02-20Bibliographically approved
Fentaye, A. D. & Kyprianidis, K. (2024). Gas turbine prognostics via Temporal Fusion Transformer. Aeronautical Journal
Open this publication in new window or tab >>Gas turbine prognostics via Temporal Fusion Transformer
2024 (English)In: Aeronautical Journal, ISSN 0001-9240Article in journal (Refereed) Epub ahead of print
Abstract [en]

Gas turbines play a vital role in various industries. Timely and accurately predicting their degradation is essential for efficient operation and optimal maintenance planning. Diagnostic and prognostic outcomes aid in determining the optimal compressor washing intervals. Diagnostics detects compressor fouling and estimates the trend up to the current time. If the forecast indicates fast progress in the fouling trend, scheduling offline washing during the next inspection event or earlier may be crucial to address the fouling deposit comprehensively. This approach ensures that compressor cleaning is performed based on its actual health status, leading to improved operation and maintenance costs. This paper presents a novel prognostic method for gas turbine degradation forecasting through a time-series analysis. The proposed approach uses the Temporal Fusion Transformer model capable of capturing time-series relationships at different scales. It combines encoder and decoder layers to capture temporal dependencies and temporal-attention layers to capture long-range dependencies across the encoded degradation trends. Temporal attention is a self-attention mechanism that enables the model to consider the importance of each time step degradation in the context of the entire degradation profile of the given health parameter. Performance data from multiple two-spool turbofan engines is employed to train and test the method. The test results show promising forecasting ability of the proposed method multiple flight cycles into the future. By leveraging the insights provided by the method, maintenance events and activities can be scheduled in a proactive manner. Future work is to extend the method to estimate remaining useful life.

Place, publisher, year, edition, pages
CAMBRIDGE UNIV PRESS, 2024
Keywords
gas turbines prognostics, remaining useful life, Temporal Fusion Transformer, compressor washing, predictive maintenance, maintenance optimisation
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-66545 (URN)10.1017/aer.2024.40 (DOI)001207525400001 ()2-s2.0-85191409334 (Scopus ID)
Available from: 2024-05-08 Created: 2024-05-08 Last updated: 2024-05-08Bibliographically approved
Zaccaria, V., Fentaye, A. D. & Kyprianidis, K. (2023). BAYESIAN INFORMATION FUSION FOR GAS TURBINES DIAGNOSTICS AND PROGNOSTICS. In: Proc. ASME Turbo Expo: . Paper presented at Proceedings of the ASME Turbo Expo. American Society of Mechanical Engineers (ASME)
Open this publication in new window or tab >>BAYESIAN INFORMATION FUSION FOR GAS TURBINES DIAGNOSTICS AND PROGNOSTICS
2023 (English)In: Proc. ASME Turbo Expo, American Society of Mechanical Engineers (ASME) , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Prognosis, or the forecasting of remaining operational life of a component, is a fundamental step for predictive maintenance of turbomachines. While diagnostics gives important information on the current conditions of the engine, it is through prognostics that a suitable maintenance interval can be determined, which is critical to minimize costs. However, mature prognostic models are still lacking in industry, which still heavily relies on human experience or generic statistical quantifications. Predicting future conditions is very challenging due to many factors that introduce significant uncertainty, including unknown future machine operations, interaction between multiple faults, and inherent errors in diagnostic and prognostic models. Given the importance to quantify this uncertainty and its impact on operational decisions, this work presents an information fusion approach for gas turbine prognostics. Condition monitoring performed by a Bayesian network is fused with a particle filter for prognosis of gas turbine degradation, and the effect of diagnostic models uncertainty on the prognosis are estimated through probabilistic analysis. Gradual and rapid degradation are simulated on a gas turbine performance model and the impact of sensor noise and initial conditions for the particle filter estimation are assessed. This work demonstrates that the combination of Bayesian networks and particle filters can give good results for short-term prognosis.

Place, publisher, year, edition, pages
American Society of Mechanical Engineers (ASME), 2023
Keywords
Bayesian inference, Information fusion, Particle filter, Prognostics, Condition monitoring, Gas turbines, Inference engines, Monte Carlo methods, Uncertainty analysis, Bayesia n networks, Bayesian information, Condition, Diagnostic model, Diagnostics and prognostics, Prognostic, Prognostic modeling, Uncertainty, Bayesian networks
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-64854 (URN)10.1115/GT2023-103171 (DOI)2-s2.0-85177194757 (Scopus ID)9780791886977 (ISBN)
Conference
Proceedings of the ASME Turbo Expo
Available from: 2023-11-29 Created: 2023-11-29 Last updated: 2023-11-29Bibliographically approved
Martinsen, M., Fentaye, A. D., Dahlquist, E. & Zhou, Y. (2023). Holistic Approach Promotes Failure Prevention of Smart Mining Machines Based on Bayesian Networks. Machines, 11(10), Article ID 940.
Open this publication in new window or tab >>Holistic Approach Promotes Failure Prevention of Smart Mining Machines Based on Bayesian Networks
2023 (English)In: Machines, E-ISSN 2075-1702, Vol. 11, no 10, article id 940Article in journal (Refereed) Published
Abstract [en]

In the forthcoming era of fully autonomous mining, spanning from drilling operations to port logistics, novel approaches will be essential to pre-empt hazardous situations in the absence of human intervention. The progression towards complete autonomy in mining operations must have meticulous approaches and uncompromised security. By ensuring a secure transition, the mining industry can navigate the transformative shift towards autonomy while upholding the highest standards of safety and operational reliability. Experiments involving autonomous pathways for mining machinery that utilize AI for route optimization demonstrate a higher speed capacity than manually operated approaches; this translates to enhanced productivity, subsequently fostering increased production capacity to meet the rising demand for metals. Nonetheless, accelerated wear on crucial elements like tires, brakes, and bearings on mining machines has been observed. Autonomous mining processes will require smarter machines without humans that guide and support actions prior to a hazardous situation occurring. This paper will delve into a comprehensive perspective on the safety of autonomous mining machines by using Bayesian networks (BN) to detect possible hazard fires. The BN is tuned with a combination of empirical field data and laboratory data. Various faults have been recognized, and their correlation with the measurements has been established.

Place, publisher, year, edition, pages
Multidisciplinary Digital Publishing Institute (MDPI), 2023
Keywords
artificial intelligence, autonomous, bayesian networks, machine learning, mining machines, predictive maintenance, safety, smart sensing
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-64701 (URN)10.3390/machines11100940 (DOI)001093749100001 ()2-s2.0-85175038225 (Scopus ID)
Available from: 2023-11-09 Created: 2023-11-09 Last updated: 2023-11-15Bibliographically approved
Salilew, W. M., Gilani, S. I., Alemu Lemma, T., Fentaye, A. D. & Kyprianidis, K. (2023). Simultaneous Fault Diagnostics for Three-Shaft Industrial Gas Turbine. Machines, 11(8), Article ID 832.
Open this publication in new window or tab >>Simultaneous Fault Diagnostics for Three-Shaft Industrial Gas Turbine
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2023 (English)In: Machines, E-ISSN 2075-1702, Vol. 11, no 8, article id 832Article in journal (Refereed) Published
Abstract [en]

The study focused on the development of -gas turbine full- and part-load operation diagnostics. The gas turbine performance model was developed using commercial software and validated using the engine manufacturer data. Upon the validation, fouling, erosion, and variable inlet guide vane drift were simulated to generate faulty data for the diagnostics development. Because the data from the model was noise-free, sensor noise was added to each of the diagnostic set parameters to reflect the actual scenario of the field operation. The data was normalized. In total, 13 single, and 61 double, classes, including 1 clean class, were prepared and used as input. The number of observations for single faults diagnostics were 1092, which was 84 for each class, and 20,496 for double faults diagnostics, which was 336 for each class. Twenty-eight machine learning techniques were investigated to select the one which outperformed the others, and further investigations were conducted with it. The diagnostics results show that the neural network group exhibited better diagnostic accuracy at both full- and part-load operations. The test results and its comparison with literature results demonstrated that the proposed method has a satisfactory and reliable accuracy in diagnosing the considered fault scenarios. The results are discussed, following the plots.

Place, publisher, year, edition, pages
Multidisciplinary Digital Publishing Institute (MDPI), 2023
Keywords
diagnostics, gas turbine, machine learning, simultaneous faults, single faults
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-64170 (URN)10.3390/machines11080832 (DOI)001056926800001 ()2-s2.0-85169141671 (Scopus ID)
Available from: 2023-09-06 Created: 2023-09-06 Last updated: 2023-09-20Bibliographically approved
Salilew, W. M., Gilani, S. I., Lemma, T. A., Fentaye, A. D. & Kyprianidis, K. (2023). Synergistic Effect of Physical Faults and Variable Inlet Guide Vane Drift on Gas Turbine Engine. Machines, 11(8), Article ID 789.
Open this publication in new window or tab >>Synergistic Effect of Physical Faults and Variable Inlet Guide Vane Drift on Gas Turbine Engine
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2023 (English)In: Machines, E-ISSN 2075-1702, Vol. 11, no 8, article id 789Article in journal (Refereed) Published
Abstract [en]

This study presents a comprehensive analysis of the impact of variable inlet guide vanes and physical faults on the performance of a three-shaft gas turbine engine operating at full load. By utilizing the input data provided by the engine manufacturer, the performance models for both the design point and off-design scenarios have been developed. To ensure the accuracy of our models, validation was conducted using the manufacturer’s data. Once the models were successfully validated, various degradation conditions, such as variable inlet guide vane drift, fouling, and erosion, were simulated. Three scenarios that cause gas turbine degradation have been considered and simulated: First, how would the variable inlet guide vane drift affect the gas turbine performance? Second, how would the combined effect of fouling and variable inlet guide vane drift cause the degradation of the engine performance? Third, how would the combined effect of erosion and variable inlet guide vane drift cause the degradation of the engine performance? The results revealed that up-VIGV drift, which is combined fouling and erosion, shows a small deviation because of offsetting the isentropic efficiency drop caused by fouling and erosion. It is clearly observed that fouling affects more upstream components, whereas erosion affects more downstream components. Furthermore, the deviation of performance and output parameters due to the combined faults has been discussed.

Place, publisher, year, edition, pages
Multidisciplinary Digital Publishing Institute (MDPI), 2023
Keywords
gas turbine, performance model, physical faults, variable inlet guide vane
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-64178 (URN)10.3390/machines11080789 (DOI)001055891800001 ()2-s2.0-85169109002 (Scopus ID)
Available from: 2023-09-06 Created: 2023-09-06 Last updated: 2023-12-04Bibliographically approved
Salilew, W. M., Abdul Karim, Z. A., Lemma, T. A., Fentaye, A. D. & Kyprianidis, K. (2023). Three Shaft Industrial Gas Turbine Transient Performance Analysis. Sensors, 23(4)
Open this publication in new window or tab >>Three Shaft Industrial Gas Turbine Transient Performance Analysis
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2023 (English)In: Sensors, E-ISSN 1424-8220, Vol. 23, no 4Article in journal (Refereed) Published
Abstract [en]

The power demand from gas turbines in electrical grids is becoming more dynamic due to the rising demand for power generation from renewable energy sources. Therefore, including the transient data in the fault diagnostic process is important when the steady-state data are limited and if some component faults are more observable in the transient condition than in the steady-state condition. This study analyses the transient behaviour of a three-shaft industrial gas turbine engine in clean and degraded conditions with consideration of the secondary air system and variable inlet guide vane effects. Different gas path faults are simulated to demonstrate how magnified the transient measurement deviations are compared with the steady-state measurement deviations. The results show that some of the key measurement deviations are considerably higher in the transient mode than in the steady state. This confirms the importance of considering transient measurements for early fault detection and more accurate diagnostic solutions.

Place, publisher, year, edition, pages
NLM (Medline), 2023
Keywords
design point, erosion, fouling, gas turbine, off-design, performance, transient
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-62036 (URN)10.3390/s23041767 (DOI)000941766500001 ()36850365 (PubMedID)2-s2.0-85148972701 (Scopus ID)
Available from: 2023-03-08 Created: 2023-03-08 Last updated: 2023-03-30Bibliographically approved
Salilew, W. M., Abdul Karim, Z. A., Lemma, T. A., Fentaye, A. D. & Kyprianidis, K. (2022). Predicting the Performance Deterioration of a Three-Shaft Industrial Gas Turbine. Entropy, 24(8), Article ID 1052.
Open this publication in new window or tab >>Predicting the Performance Deterioration of a Three-Shaft Industrial Gas Turbine
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2022 (English)In: Entropy, E-ISSN 1099-4300, Vol. 24, no 8, article id 1052Article in journal (Refereed) Published
Abstract [en]

The gas turbine was one of the most important technological developments of the early 20th century, and it has had a significant impact on our lives. Although some researchers have worked on predicting the performance of three-shaft gas turbines, the effects of the deteriorated components on other primary components and of the physical faults on the component measurement parameters when considering the variable inlet guide valve scheduling and secondary air system for three-shaft gas turbine engines have remained unexplored. In this paper, design point and off-design performance models for a three-shaft gas turbine were developed and validated using the GasTurb 13 commercial software. Since the input data were limited, some engineering judgment and optimization processes were applied. Later, the developed models were validated using the engine manufacturer's data. Right after the validation, using the component health parameters, the physical faults were implanted into the non-linear steady-state model to investigate the performance of the gas turbine during deterioration conditions. The effects of common faults, namely fouling and erosion in primary components of the case study engine, were simulated during full-load operation. The fault simulation results demonstrated that as the severity of the fault increases, the component performance parameters and measurement parameters deviated linearly from the clean state. Furthermore, the sensitivity of the measurement parameters to the fault location and type were discussed, and as a result they can be used to determine the location and kind of fault during the development of a diagnosis model.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
gas turbine, design point, off-design, steady-state, performance, physical faults
National Category
Other Mechanical Engineering
Identifiers
urn:nbn:se:mdh:diva-59891 (URN)10.3390/e24081052 (DOI)000845943700001 ()36010716 (PubMedID)2-s2.0-85137369822 (Scopus ID)
Available from: 2022-09-08 Created: 2022-09-08 Last updated: 2023-03-28Bibliographically approved
Salilew, W. M., Karim, Z. A., Lemma, T. A., Fentaye, A. D. & Kyprianidis, K. (2022). The Effect of Physical Faults on a Three-Shaft Gas Turbine Performance at Full- and Part-Load Operation. Sensors, 22(19), Article ID 7150.
Open this publication in new window or tab >>The Effect of Physical Faults on a Three-Shaft Gas Turbine Performance at Full- and Part-Load Operation
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2022 (English)In: Sensors, E-ISSN 1424-8220, Vol. 22, no 19, article id 7150Article in journal (Refereed) Published
Abstract [en]

A gas path analysis approach of dynamic modelling was used to examine the gas turbine performance. This study presents an investigation of the effect of physical faults on the performance of a three-shaft gas turbine at full-load and part-load operation. A nonlinear steady state performance model was developed and validated. The datasheet from the engine manufacturer was used to gather the input and validation data. Some engineering judgement and optimization were used. Following validation of the engine performance model with the engine manufacturer data using physical fault and component health parameter relationships, physical faults were implanted into the performance model to evaluate the performance characteristics of the gas turbine at degradation state at full- and part-load operation. The impact of erosion and fouling on the gas turbine output parameters, component measurement parameters, and the impact of degraded components on another primary component of the engine have been investigated. The simulation results show that the deviation in the output parameters and component isentropic efficiency due to compressor fouling and erosion is linear with the load variation, but it is almost nonlinear for the downstream components. The results are discussed following the plots.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
gas turbine, performance model, steady-state, full-load, part-load performance, physical faults
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-60388 (URN)10.3390/s22197150 (DOI)000867322000001 ()36236249 (PubMedID)2-s2.0-85140029720 (Scopus ID)
Available from: 2022-10-26 Created: 2022-10-26 Last updated: 2022-11-17Bibliographically approved
Fentaye, A. D., Zaccaria, V. & Kyprianidis, K. (2021). Aircraft engine performance monitoring and diagnostics based on deep convolutional neural networks. Machines, 9(12), Article ID 337.
Open this publication in new window or tab >>Aircraft engine performance monitoring and diagnostics based on deep convolutional neural networks
2021 (English)In: Machines, E-ISSN 2075-1702, Vol. 9, no 12, article id 337Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2021
Keywords
Convolutional neural network, Diagnostics, Fault detection and isolation, Gas turbine, Gradual degradation, Rapid faults
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-56878 (URN)10.3390/machines9120337 (DOI)000738099500001 ()2-s2.0-85121681459 (Scopus ID)
Note

Export Date: 12 January 2022; Article; Correspondence Address: Fentaye, A.D.; Future Energy Center, Sweden; email: amare.desalegn.fentaye@mdh.se; Funding details: Stiftelsen för Kunskaps- och Kompetensutveckling, KKS, 20190994; Funding text 1: Funding: This research was funded by the Swedish Knowledge Foundation (KKS) under the project PROGNOSIS, Grant Number 20190994.

Available from: 2022-01-12 Created: 2022-01-12 Last updated: 2023-03-28Bibliographically approved
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