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Estimation and Mitigation of Unknown Airplane Installation Effects on GPA Diagnostics
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. SAAB Aeronut, S-58254 Linkoping, Sweden..ORCID iD: 0000-0003-0739-8448
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-8466-356X
2022 (English)In: Machines, E-ISSN 2075-1702, Vol. 10, no 1, article id 36Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI , 2022. Vol. 10, no 1, article id 36
Keywords [en]
gas turbine diagnostics, gas path analysis, installation effects, neural networks
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-57249DOI: 10.3390/machines10010036ISI: 000749817000001Scopus ID: 2-s2.0-85123529126OAI: oai:DiVA.org:mdh-57249DiVA, id: diva2:1636207
Available from: 2022-02-09 Created: 2022-02-09 Last updated: 2023-03-28Bibliographically approved
In thesis
1. On model based aero engine diagnostics
Open this publication in new window or tab >>On model based aero engine diagnostics
2023 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Västerås: Mälardalens universitet, 2023. p. 73
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 334
Keywords
Aero engines, model based diagnostics, gas path analysis
National Category
Aerospace Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-61274 (URN)978-91-7485-578-4 (ISBN)
Presentation
2023-01-31, Gamma, Mälardalens universitet, Västerås, 09:00 (English)
Opponent
Supervisors
Funder
Knowledge Foundation
Available from: 2022-12-15 Created: 2022-12-14 Last updated: 2023-01-10Bibliographically approved

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Stenfelt, MikaelKyprianidis, Konstantinos

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