Details
| Originalsprache | Englisch |
|---|---|
| Seitenumfang | 9 |
| Fachzeitschrift | International Journal of Gas Turbine, Propulsion and Power Systems |
| Jahrgang | 12 |
| Ausgabenummer | 4 |
| Publikationsstatus | Veröffentlicht - Dez. 2021 |
Abstract
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Maschinenbau
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in: International Journal of Gas Turbine, Propulsion and Power Systems, Jahrgang 12, Nr. 4, 12.2021.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Experimental and Numerical Based Defect Detection in a Model Combustion Chamber through Machine Learning
AU - von der Haar, Henrik
AU - Ignatidis, Panagiotis
AU - Dinkelacker, Friedrich
N1 - Funding Information: The subproject A6 “Impact of Mixing on the Signature of Combustor Defects” is part of the Collaborative Research Center CRC 871 “Regeneration of complex capital goods”. Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 871/3 – 119193472
PY - 2021/12
Y1 - 2021/12
N2 - A disturbed combustion process in an aircraft engine has an impact on the internal flow and leads to specific irregularities in the species distribution in the exhaust jet. Measuring this distribution provides information about the combustion state and offers the possibility to reduce the engine down-time during inspection. The approach has the potential to improve the resource management as well as the availability and safety of the system. Aim of the research project is to evaluate the state of an aircraft engine by analyzing the emission field in the exhaust jet and using a support vector machine (SVM) algorithm for automatic defect detection and allocation.
AB - A disturbed combustion process in an aircraft engine has an impact on the internal flow and leads to specific irregularities in the species distribution in the exhaust jet. Measuring this distribution provides information about the combustion state and offers the possibility to reduce the engine down-time during inspection. The approach has the potential to improve the resource management as well as the availability and safety of the system. Aim of the research project is to evaluate the state of an aircraft engine by analyzing the emission field in the exhaust jet and using a support vector machine (SVM) algorithm for automatic defect detection and allocation.
UR - http://www.scopus.com/inward/record.url?scp=85123750090&partnerID=8YFLogxK
U2 - 10.38036/JGPP.12.4_1
DO - 10.38036/JGPP.12.4_1
M3 - Article
VL - 12
JO - International Journal of Gas Turbine, Propulsion and Power Systems
JF - International Journal of Gas Turbine, Propulsion and Power Systems
IS - 4
ER -