| 1. |
- 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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| 2. |
- 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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