| 1. |
- Fentaye, Amare Desalegn, et al.
(författare)
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Hybrid model-based and data-driven diagnostic algorithm for gas turbine engines
- 2020
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Ingår i: Proceedings of the ASME Turbo Expo. - : American Society of Mechanical Engineers (ASME). - 9780791884140
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Konferensbidrag (refereegranskat)abstract
- Data-driven algorithms require large and comprehensive training samples in order to provide reliable diagnostic solutions. However, in many gas turbine applications, it is hard to find fault data due to proprietary and liability issues. Operational data samples obtained from end-users through collaboration projects do not represent fault conditions sufficiently and are not labeled either. Conversely, model-based methods have some accuracy deficiencies due to measurement uncertainty and model smearing effects when the number of gas path components to be assessed is large. The present paper integrates physics-based and data-driven approaches aiming to overcome this limitation. In the proposed method, an adaptive gas path analysis (AGPA) is used to correct measurement data against the ambient condition variations and normalize. Fault signatures drawn from the AGPA are used to assess the health status of the case engine through a Bayesian network (BN) based fault diagnostic algorithm. The performance of the proposed technique is evaluated based on five different gas path component faults of a three-shaft turbofan engine, namely intermediate-pressure compressor fouling (IPCF), high-pressure compressor fouling (HPCF), high-pressure turbine erosion (HPTE), intermediate-pressure turbine erosion (IPTE), and low-pressure turbine erosion (LPTE). Robustness of the method under measurement uncertainty has also been tested using noise-contaminated data. Moreover, the fault diagnostic effectiveness of the BN algorithm on different number and type of measurements is also examined based on three different sensor groups. The test results verify the effectiveness of the proposed method to diagnose single gas path component faults correctly even under a significant noise level and different instrumentation suites. This enables to accommodate measurement suite inconsistencies between engines of the same type. The proposed method can further be used to support the gas turbine maintenance decision-making process when coupled with overall Engine Health Management (EHM) systems.
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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. |
- Stenfelt, Mikael, et al.
(författare)
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Estimation and Mitigation of Unknown Airplane Installation Effects on GPA Diagnostics
- 2022
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Ingår i: Machines. - : MDPI. - 2075-1702. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- In gas turbines used for airplane propulsion, the number of sensors are kept at a minimum for accurate control and safe operation. Additionally, when data are communicated between the airplane main computer and the various subsystems, different systems may have different constraints and requirements regarding what data transmit. Early in the design process, these parameters are relatively easy to change, compared to a mature product. If the gas turbine diagnostic system is not considered early in the design process, it may lead to diagnostic functions having to operate with reduced amount of data. In this paper, a scenario where the diagnostic function cannot obtain airplane installation effects is considered. The installation effects in question is air intake pressure loss (pressure recovery), bleed flow and shaft power extraction. A framework is presented where the unknown installation effects are estimated based on available data through surrogate models, which is incorporated into the diagnostic framework. The method has been evaluated for a low-bypass turbofan with two different sensor suites. It has also been evaluated for two different diagnostic schemes, both determined and underdetermined. Results show that, compared to assuming a best-guess constant-bleed and shaft power, the proposed method reduce the RMS in health parameter estimation from 26% up to 80% for the selected health parameters. At the same time, the proposed method show the same degradation pattern as if the installation effects were known.
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| 4. |
- Stenfelt, Mikael, et al.
(författare)
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Gas Turbine Mixer Modelling Strategies and Afterburner Liner Burn-Through Diagnostics
- 2019
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- A mixer may be damaged either by cracks or mechanical deformation causing a change in geometry. Only the latter case is, in some cases, possible to detect by shifts in physical measurements such as pressures and temperatures. In general, deformations of the geometry of a mixer due to damage is very hard to identify and quantify with the onboard measurements available, especially for turbofans with high bypass ratio (BPR) where a damaged mixer may cause a slight loss in thrust rather than shifts in measurable quantities.A special case of mixer damage that may be detected is burn-through of the afterburner liner in low bypass afterburning turbofans. The liner is used to protect the outer casing of the gas turbine to the high temperatures during afterburner operation. For this, the liner need to be continuously cooled by bypass air to withstand the temperatures. A burn-through is generally caused by a local blockage of the cooling path, leading to temperatures the liner cannot withstand. In severe cases it may cause a burn-through of the gas turbine outer casing as well where it may cause a fire in the engine bay.In this paper, two diagnostic routines are developed to identify a burn-through of an afterburner liner. The diagnostics is intended to be performed as a part of the startup check of the gas turbine to increase the confidence that no burn-through has occurred during the last operation. For these methods a mixer model of high enough fidelity is required, which is described in the paper. The main conclusion is that with enough data it is possible to detect a burn-through but the data collection time is so long that the methods need to be further enhanced to be of any practical use.
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| 5. |
- Stenfelt, Mikael, 1983-
(författare)
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On model based aero engine diagnostics
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Maintenance and diagnostics play a vital role in the aviation sector. This is especially true for the engines, being one of the most vital components. Lack of maintenance, or poor knowledge of the current health status of the engines, may lead to unforeseen disruptions and possibly catastrophic effects. To keep track of the health status, and thereby supporting maintenance planning, model based diagnostics is a key factor. In the work going into this thesis, various aspects of model based gas turbine diagnostics, focused on aero engines, are covered. First, the importance of knowing what health parameters may be derived from a set of measurements is addressed. The selected combination is herein denoted as a matching scheme. A framework is proposed where the most suitable matching scheme is selected for a numerically robust diagnostic system. If a sensor malfunction is detected, the system automatically adapts.The second subject is a system for detecting a burn-through of an afterburner inner liner. This kind of burn-through event has a very small impact on available on-board measurements, making it difficult to detect numerically. A method is proposed performing back-to-back testing after each engine start. The method has shown potential to detect major burn-through events under the preconditions, regarding data collection time and frequency. Increasing these will allow for more accurate estimations.The third subject covers the importance of knowing the airplane installation effects. These are generally the intake pressure recovery, bleed and shaft power extraction. Just like inaccurate measurements affect diagnostic results, so does erroneous installation effects. A method for estimating said effects in the presence of gradual degradation has been proposed by using neural networks. By retraining the networks throughout the degradation process, the estimation errors is reduced, ensuring relevant estimations even at severe degradations.Finally, an issue related to the general lack of on-board measurements for diagnostics is addressed. Due to lack of measurements, the diagnostic model tend to be underdetermined. A least square solver working without a priori information has been implemented and evaluated. Results from the solver is very much dependent on available instrumentation. In well instrumented components, such as the compressors, good diagnostic accuracy was achieved while the turbine health estimations suffer from smeared out results due to poor instrumentation.
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| 6. |
- 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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| 7. |
- Zaccaria, Valentina, 1989-, et al.
(författare)
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Fleet monitoring and diagnostics framework based on digital twin of aero-engines
- 2018
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Ingår i: Proceedings of the ASME Turbo Expo. - : American Society of Mechanical Engineers (ASME). - 9780791851128
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Konferensbidrag (refereegranskat)abstract
- Monitoring aircraft performance in a fleet is fundamental to ensure optimal operation and promptly detect anomalies that can increase fuel consumption or compromise flight safety. Accurate failure detection and life prediction methods also result in reduced maintenance costs. The major challenges in fleet monitoring are the great amount of collected data that need to be processed and the variability between engines of the fleet, which requires adaptive models. In this paper, a framework for monitoring, diagnostics, and health management of a fleet of aircrafts is proposed. The framework consists of a multi-level approach: starting from thresholds exceedance monitoring, problematic engines are isolated, on which a fault detection system is then applied. Different methods for fault isolation, identification, and quantification are presented and compared, and the related challenges and opportunities are discussed. This conceptual strategy is tested on fleet data generated through a performance model of a turbofan engine, considering engine-to-engine and flight-to-flight variations and uncertainties in sensor measurements. Limitations of physics-based methods and machine learning techniques are investigated and the needs for fleet diagnostics are highlighted.
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| 8. |
- Zaccaria, Valentina, 1989-, et al.
(författare)
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Probabilistic model for aero-engines fleet condition monitoring
- 2020
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Ingår i: Aerospace. - : MDPI Multidisciplinary Digital Publishing Institute. - 2226-4310. ; 7:6
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Tidskriftsartikel (refereegranskat)abstract
- Since aeronautic transportation is responsible for a rising share of polluting emissions, it is of primary importance to minimize the fuel consumption any time during operations. From this perspective, continuous monitoring of engine performance is essential to implement proper corrective actions and avoid excessive fuel consumption due to engine deterioration. This requires, however, automated systems for diagnostics and decision support, which should be able to handle large amounts of data and ensure reliability in all the multiple conditions the engines of a fleet can be found in. In particular, the proposed solution should be robust to engine-to-engine deviations and dierent sensors availability scenarios. In this paper, a probabilistic Bayesian network for fault detection and identification is applied to a fleet of engines, simulated by an adaptive performance model. The combination of the performance model and the Bayesian network is also studied and compared to the probabilistic model only. The benefit in the suggested hybrid approach is identified as up to 50% higher accuracy. Sensors unavailability due to manufacturing constraints or sensor faults reduce the accuracy of the physics-based method, whereas the Bayesian model is less aected.
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