| 1. |
- Zaccaria, Valentina, 1989-, et al.
(författare)
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A review of information fusion methodsfor gas turbine diagnostics
- 2019
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Ingår i: Sustainability. - : MDPI AG. - 2071-1050. ; 11:22
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Forskningsöversikt (refereegranskat)abstract
- The correct and early detection of incipient faults or severe degradation phenomena in gas turbine systems is essential for safe and cost-effective operations. A multitude of monitoring and diagnostic systems were developed and tested in the last few decades. The current computational capability of modern digital systems was exploited for both accurate physics-based methods and artificial intelligence or machine learning methods. However, progress is rather limited and none of the methods explored so far seem to be superior to others. One solution to enhance diagnostic systems exploiting the advantages of various techniques is to fuse the information coming from different tools, for example, through statistical methods. Information fusion techniques such as Bayesian networks, fuzzy logic, or probabilistic neural networks can be used to implement a decision support system. This paper presents a comprehensive review of information and decision fusion methods applied to gas turbine diagnostics and the use of probabilistic reasoning to enhance diagnostic accuracy. The different solutions presented in the literature are compared, and major challenges for practical implementation on an industrial gas turbine are discussed. Detecting and isolating faults in a system is a complex problem with many uncertainties, including the integrity of available information. The capability of different information fusion techniques to deal with uncertainty are also compared and discussed. Based on the lessons learned, new perspectives for diagnostics and a decision support system are proposed.
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| 2. |
- Stenfelt, Mikael, 1983-, et al.
(författare)
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AUTOMATIC GAS TURBINE MATCHING SCHEME ADAPTATION FOR ROBUST GPA DIAGNOSTICS
- 2019
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Ingår i: Proceedings of the ASME Turbo ExpoVolume 6, 2019.
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Konferensbidrag (refereegranskat)abstract
- When performing gas turbine diagnostics using Gas Path Analysis (GPA), a convenient way of extracting the degradations is by feeding the measured data from a gas turbine to a well-tuned gas turbine performance code, which in turn calculates the deltas on the chosen health parameters matching the measured inputs. For this, a set of measured parameters must be matched with suitable health parameters, such as deltas on compressor and turbine efficiency and flow capacity.In aero engines, the number of sensors are in general limited due to cost and weight constraints and only the necessary sensors for safe engine operation are available. Some important sensors may have redundancy in case of a sensor loss but it is far from certain that this applies to all sensors available.If a sensor malfunctions by giving false or no values, the functions using the sensor will be negatively affected in some way causing them to either synthesize a fictive measurement, changing operating scheme, going into a degraded operating mode or shutting down parts or the whole process. If an onboard diagnostic algorithm fails due to sensor faults it will lead to a decrease in flight safety, thus there is a need for a robust system.This paper presents a strategy for automatic modifications of the gas turbine diagnostic matching scheme when sensors malfunction to ensure a robust function. When a sensor fault is detected and classified as malfunctioning, the gas turbine matching scheme is modified according to predefined rules. If possible, a redundant measurement replaces the faulty measurement. If not, the matching scheme will be modified by determining if any health parameters cannot be derived by the functional set of measurements and remove the least valuable health parameter while maintaining a working matching scheme for the remaining health parameters.
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| 3. |
- Zaccaria, Valentina, 1989-, et al.
(författare)
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A MODEL-BASED SOLUTION FOR GAS TURBINE DIAGNOSTICS: SIMULATIONS AND EXPERIMENTAL VERIFICATION
- 2019
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Ingår i: Proceedings of the ASME Turbo ExpoVolume 6, 2019. - 9780791858677
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Konferensbidrag (refereegranskat)abstract
- Prompt detection of incipient faults and accurate monitoring of engine deterioration are key aspects for ensuring safe operations and planning a timely maintenance. Modern computing capabilities allow for more and more complex tools for engine monitoring and diagnostics. Nevertheless, an underlying physics-based approach is often preferable, because not only the “what” but also the “why” can be identified, providing an effective decision support tool to the service engineer. In this work, a physics-based adaptive model is used to evaluate performance deltas and correct the data to reference conditions (gas turbine load and ambient conditions), while a data-driven correlation algorithm identifies the most likely matches within a fault signatures database. Possible faults are ordered from the highest correlation in the decision support system and the most likely fault can be selected based on the number of occurrences and the associated correlation. Gradual engine degradation can also be monitored by displaying performance deltas trends during time. The diagnostics tool was tested on a validated performance model of a single-shaft industrial gas turbine and subsequently on experimental data. This paper presents the diagnostics system structure, the model adaptation scheme, and the results obtained from simulated and real fault data. Accurate fault isolation and severity identification were achieved in all cases, demonstrating the tool capability for decision support system.
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| 4. |
- Zaccaria, Valentina, 1989-, et al.
(författare)
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ADAPTIVE CONTROL OF MICRO GAS TURBINE FOR ENGINE DEGRADATION COMPENSATION
- 2019
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Ingår i: PROCEEDINGS OF THE ASME TURBO EXPO. - : AMER SOC MECHANICAL ENGINEERS.
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Konferensbidrag (refereegranskat)abstract
- Micro gas turbine engines in the range of 1-100 kW are playing a key role in distributed generation applications, due to the high reliability and quick load following that favor their integration with intermittent renewable sources. Micro-CHP systems based on gas turbine technology are obtaining a higher share in the market and are aiming at reducing the costs and increasing energy conversion efficiency. An effective control of system operating parameters during the whole engine lifetime is essential to maintain desired performance and at the same time guarantee safe operations. Because of the necessity to reduce the costs, fewer sensors are usually available than in standard industrial gas turbines, limiting the choice of control parameters. This aspect is aggravated by engine aging and deterioration phenomena that change operating performance from the expected one. In this situation, a control architecture designed for healthy operations may not be adequate anymore, because the relationship between measured parameters and unmeasured variables (e.g. turbine inlet temperature or efficiency) varies depending on the level of engine deterioration. In this work, an adaptive control scheme is proposed to compensate the effects of engine degradation over the lifetime. Component degradation level is monitored by a diagnostic tool that estimates performance variations from available measurements; then, the information on the gas turbine health condition is used by an observer-based model predictive controller to maintain the machine in a safe range of operation and limit the reduction in system efficiency.
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