| 1. |
- Aslanidou, Ioanna, et al.
(författare)
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Micro Gas Turbines in the Future Smart Energy System : Fleet Monitoring, Diagnostics, and System Level Requirements
- 2021
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Ingår i: Frontiers in Mechanical Engineering. - : Frontiers Media S.A.. - 2297-3079. ; 7
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Forskningsöversikt (refereegranskat)abstract
- The energy generation landscape is changing, pushed by stricter regulations for emissions control and green energy generation. The limitations of renewable energy sources, however, require flexible energy production sources to supplement them. Micro gas turbine based combined heat and power plants, which are used for domestic applications, can fill this gap if they become more reliable. This can be achieved with the use of an engine monitoring and diagnostics system: real-time engine condition monitoring and fault diagnostics results in reduced operating and maintenance costs and increased component and engine life. In order to allow the step change in the connection of small engines to the grid, a fleet monitoring system for micro gas turbines is required. A proposed framework combines a physics-based model and a data-driven model with machine learning capabilities for predicting system behavior, and includes a purpose-developed diagnostic tool for anomaly detection and classification for a multitude of engines. The framework has been implemented on a fleet of micro gas turbines and some of the lessons learned from the demonstration of the concept as well as key takeaways from the general literature are presented in this paper. The extension of fleet monitoring to optimal operation and production planning in relation to the needs of the grid will allow the micro gas turbines to fit in the future green energy system, connect to the grid, and trade in the energy market. The requirements on the system level for the widespread use of micro gas turbines in the energy system are addressed in the paper. A review of the current solutions in fleet monitoring and diagnostics, generally developed for larger engines, is included, with an outlook into a sustainable future.
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| 2. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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Dilemmas in designing e-learning experiences for professionals
- 2021
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Ingår i: Proceedings of the European Conference on e-Learning, ECEL. ; , s. 10-17
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Konferensbidrag (refereegranskat)abstract
- The aims of this research are to enhance industry-university collaboration and to design learning experiences connecting the research front to practitioners. We present an empirical study with a qualitative approach involving teachers who gathered data from newly developed advanced level courses in artificial intelligence, energy, environmental, and systems engineering. The study is part of FutureE, an academic development project over 3 years involving 12 courses. The project, as well as this study, is part of a cross-disciplinary collaboration effort. Empirical data comes from course evaluations, course analysis, teacher workshops, and semi-structured interviews with selected students, who are also professionals. This paper will discuss course design and course implementation by presenting dilemmas and paradoxes. Flexibility is key for the completion of studies while working. Academia needs to develop new ways to offer flexible education for students from a professional context, but still fulfil high quality standards and regulations as an academic institution. Student-to-student interactions are often suggested as necessary for qualified learning, and students support this idea but will often not commit to it during courses. Other dilemmas are micro-sized learning versus vast knowledge, flexibility versus deadlines as motivating factors, and feedback hunger versus hesitation to share work. Furthermore, we present the challenges of providing equivalent online experience to practical in-person labs. On a structural level, dilemmas appear in the communication between university management and teachers. These dilemmas are often the result of a culture designed for traditional campus education. We suggest a user-oriented approach to solve these dilemmas, which involves changes in teacher roles, culture, and processes. The findings will be relevant for teachers designing and running courses aiming to attract professionals. They will also be relevant for university management, building a strategy for lifelong e-learning based on co-creation with industry.
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| 3. |
- 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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| 4. |
- 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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| 5. |
- Ferrari, M. L., et al.
(författare)
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Pressurized SOFC System Fuelled by Biogas : Control Approaches and Degradation Impact
- 2021
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Ingår i: Journal of engineering for gas turbines and power. - : American Society of Mechanical Engineers (ASME). - 0742-4795 .- 1528-8919. ; 143:6
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Tidskriftsartikel (refereegranskat)abstract
- This paper shows control approaches for managing a pressurized solid oxide fuel cell (SOFC) system fuelled by biogas. This is an advanced solution to integrate the high efficiency benefits of a pressurized SOFC with a renewable source. The operative conditions of these analyses are based on the matching with an emulator rig including a T100 machine for tests in cyber-physical mode. So, this paper presents a real-time model including the fuel cell, the off-gas burner (OFB), and the recirculation lines. Although the microturbine components are planned to be evaluated with the hardware devices, the model includes also the T100 expander for machine control reasons. The simulations shown in this paper regard the assessment of an innovative control tool based on the model predictive control (MPC) technology. This controller and an additional tool based on the coupling of MPC and proportional integral derivative (PID) approaches were assessed against the application of PID controllers. The control targets consider both steady-state and dynamic aspects. Moreover, different control solutions are presented to operate the system during fuel cell degradation. The results include the system response to load variations, and SOFC voltage decrease. Considering the simulations including SOFC degradation, the MPC was able to decrease the thermal stress, but it was not able to compensate the degradation. On the other hand, the tool based on the coupling of the MPC and the PID approaches produced the best results in terms of set-point matching, and SOFC thermal stress containment.
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| 6. |
- Mantel, Luca, et al.
(författare)
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A Degradation Diagnosis Method for Gas Turbine-Fuel Cell Hybrid Systems Using Bayesian Networks
- 2021
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Ingår i: Journal of engineering for gas turbines and power. - : ASME. - 0742-4795 .- 1528-8919. ; 143:5
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Tidskriftsartikel (refereegranskat)abstract
- This paper aims to develop and test Bayesian belief network-based diagnosis methods, which can be used to predict the most likely degradation levels of turbine, compressor, and fuel cell (FC) in a hybrid system based on different sensors measurements. The capability of the diagnosis systems to understand if an abnormal measurement is caused by a component degradation or by a sensor fault is also investigated. The data used both to train and to test the networks are generated from a deterministic model and later modified to consider noise or bias in the sensors. The application of Bayesian belief networks (BBNs) to fuel cell-gas turbine hybrid systems is novel, thus the results obtained from this analysis could be a significant starting point to understand their potential. The diagnosis systems developed for this work provide essential information regarding levels of degradation and presence of faults in a gas turbine, fuel cell and sensors in a fuel cell-gas turbine hybrid system. The Bayesian belief networks proved to have a good level of accuracy for all the scenarios considered, regarding both steady-state and transient operations. This analysis also suggests that in the future a Bayesian belief network could be integrated with the control system to achieve safer and more efficient operations of these plants.
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| 7. |
- Marais, Heidi Lynn, 1996-, et al.
(författare)
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Detectability of Fault Signatures in a Wastewater Treatment Process
- 2021
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Ingår i: Proceedings of The First SIMS EUROSIM Conference on Modelling and Simulation, SIMS EUROSIM 2021, and 62nd International Conference of Scandinavian Simulation Society, SIMS 2021. - : Scandinavian Simulation Society and Linköping University Electronic Press. - 9789179292195 ; , s. 418-423
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Konferensbidrag (refereegranskat)abstract
- In a wastewater treatment plant reliable fault detection is an integral component of process supervision and ensuring safe operation of the process. Detecting and isolating process faults requires that sensors in the process can be used to uniquely identify such faults. However, sensors in the wastewater treatment process operate in hostile environments and often require expensive equipment and maintenance. This work addresses this problem by identifying a minimal set of sensors which can detect and isolate these faults in the Benchmark Simulation Model No. 1.Residual-based fault signatures are used to determine this sensor set using a graph-based approach; these fault signatures can be used in future work developing fault detection methods. It is recommended that further work investigate what sizes of faults are critical to detect based on their potential effects on the process, as well as ways to select an optimal sensor set from multiple valid configurations.
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| 8. |
- Zaccaria, Valentina, 1989-, et al.
(författare)
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Assessment of Dynamic Bayesian Models for Gas Turbine Diagnostics, Part 1 : Prior Probability Analysis
- 2021
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Ingår i: Machines. - : MDPI. - 2075-1702. ; 9:11
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Tidskriftsartikel (refereegranskat)abstract
- The reliability and cost-effectiveness of energy conversion in gas turbine systems are strongly dependent on an accurate diagnosis of possible process and sensor anomalies. Because data collected from a gas turbine system for diagnosis are inherently uncertain due to measurement noise and errors, probabilistic methods offer a promising tool for this problem. In particular, dynamic Bayesian networks present numerous advantages. In this work, two Bayesian networks were developed for compressor fouling and turbine erosion diagnostics. Different prior probability distributions were compared to determine the benefits of a dynamic, first-order hierarchical Markov model over a static prior probability and one dependent only on time. The influence of data uncertainty and scatter was analyzed by testing the diagnostics models on simulated fleet data. It was shown that the condition-based hierarchical model resulted in the best accuracy, and the benefit was more significant for data with higher overlap between states (i.e., for compressor fouling). The improvement with the proposed dynamic Bayesian network was 8 percentage points (in classification accuracy) for compressor fouling and 5 points for turbine erosion compared with the static network.
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| 9. |
- Zaccaria, Valentina, 1989-, et al.
(författare)
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Assessment of dynamic bayesian models for gas turbine diagnostics, part 2 : Discrimination of gradual degradation and rapid faults
- 2021
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Ingår i: Machines. - : MDPI. - 2075-1702. ; 9:12
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Tidskriftsartikel (refereegranskat)abstract
- There are many challenges that an effective diagnostic system must overcome for successful fault diagnosis in gas turbines. Among others, it has to be robust to engine-to-engine variations in the fleet, it has to discriminate between gradual deterioration and abrupt faults, and it has to identify sensor faults correctly and be robust in case of such faults. To combine their benefits and overcome their limitations, two diagnostic methods were integrated in this work to form a multi-layer system. An adaptive performance model was used to track gradual deterioration and detect rapid or abrupt anomalies, while a series of static and dynamic Bayesian networks were integrated to identify component degradation, component abrupt faults, and sensor faults. The proposed approach was tested on synthetic data and field data from a single-shaft gas turbine of 50 MW class. The results showed that the approach could give acceptable accuracy in the isolation and identification of multiple faults, with 99% detection and isolation accuracy and 1% maximum error in the identified fault magnitude. The approach was also proven robust to sensor faults, by replacing the faulty signal with an estimated value that had only 3% error compared to the real measurement.
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