Experimental and Numerical Based Defect Detection in a Model Combustion Chamber through Machine Learning

Medientyp: Sonstige Veröffentlichung; E-Artikel
Titel: Experimental and Numerical Based Defect Detection in a Model Combustion Chamber through Machine Learning
Beteiligte: von der Haar, Henrik [Verfasser:in]; Ignatidis, Panagiotis [Verfasser:in]; Dinkelacker, Friedrich [Verfasser:in]
Erschienen: Tōkyō : [Verlag nicht ermittelbar], 2021
Erschienen in: International Journal of Gas Turbine, Propulsion and Power Systems 12 (2021), Nr. 4 ; International Journal of Gas Turbine, Propulsion and Power Systems
Ausgabe: published Version
Sprache: Englisch
DOI: https://doi.org/10.15488/16726; https://doi.org/10.38036/jgpp.12.4_1
Schlagwörter: Engines ; Internal flows ; Support vector machines ; Defects ; Aircraft detection ; Combustion state ; Machine-learning ; Species distributions ; Support vector machines algorithms ; Defect detection ; Aircraft engines ; Down time ; Combustion pro-cess ; Resource management ; Automatic defect detections
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Beschreibung: 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.
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Rechte-/Nutzungshinweise: Namensnennung (CC BY)

Experimental and Numerical Based Defect Detection in a Model Combustion Chamber through Machine Learning - [published Version]

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