A significant challenge in improving the regeneration process of jet engines is the reduction of engine down-time during inspection. As such, early defect detection without engine disassembly will speed up the regeneration process. Defects in the engines hot-gas path (HGP) influence the density distribution of the flow and lead to irregularities in the density distribution of the exhaust jet which can be detected with the optical Background-Oriented Schlieren (BOS) method in a tomographic set-up. The present paper proposes a combination of tomographic BOS measurements and supervised learning algorithms to develop a methodology for an automatic defect detection system. In a first step, the methodology is verified by analyzing the exhaust jet of a swirl burner array with a non-uniform fuel-supply of single burners with tomographic BOS measurements. The measurements are used to implement a Support Vector Machine (SVM) pattern recognition algorithm. It is shown that the reconstruction quality of tomographic BOS measurements is high enough to be combined with pattern recognition algorithms. The results strengthen the hypothesis, that it is possible to automatically detect defects in jet engines with tomographic BOS measurements and pattern recognition algorithms.
Automatic Detection of Defects in a Swirl Burner Array Through an Exhaust Jet Pattern Analysis
2016
Aufsatz (Zeitschrift)
Englisch
Automatic Detection of Defects in a Swirl Burner Array Through an Exhaust Jet Pattern Analysis
Online Contents | 2017
|Experimental Defect Detection in a Swirl-Burner Array Through Exhaust Jet Analysis (AIAA 2018-0303)
British Library Conference Proceedings | 2018
|Aerodynamic Character of Dual Channel Swirl Burner
British Library Online Contents | 1998
|Flow Dynamics in a Triple Swirl Burner
Springer Verlag | 2022
|