Details
| Original language | English |
|---|---|
| Article number | 031504 |
| Journal | Journal of Engineering for Gas Turbines and Power |
| Volume | 139 |
| Issue number | 3 |
| Publication status | Published - 27 Sept 2017 |
Abstract
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 setup. 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 the first step, the methodology is tested by analyzing the exhaust jet of a swirl burner array with a nonuniform 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.
ASJC Scopus subject areas
- Energy(all)
- Nuclear Energy and Engineering
- Energy(all)
- Fuel Technology
- Engineering(all)
- Aerospace Engineering
- Energy(all)
- Energy Engineering and Power Technology
- Engineering(all)
- Mechanical Engineering
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In: Journal of Engineering for Gas Turbines and Power, Vol. 139, No. 3, 031504, 27.09.2017.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Automatic Detection of Defects in a Swirl Burner Array Through an Exhaust Jet Pattern Analysis
AU - Hartmann, Ulrich
AU - Hennecke, Christoph
AU - Dinkelacker, Friedrich
AU - Seume, Joerg R.
PY - 2017/9/27
Y1 - 2017/9/27
N2 - 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 setup. 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 the first step, the methodology is tested by analyzing the exhaust jet of a swirl burner array with a nonuniform 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.
AB - 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 setup. 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 the first step, the methodology is tested by analyzing the exhaust jet of a swirl burner array with a nonuniform 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.
UR - http://www.scopus.com/inward/record.url?scp=84992107546&partnerID=8YFLogxK
U2 - 10.1115/1.4034449
DO - 10.1115/1.4034449
M3 - Article
AN - SCOPUS:84992107546
VL - 139
JO - Journal of Engineering for Gas Turbines and Power
JF - Journal of Engineering for Gas Turbines and Power
SN - 0742-4795
IS - 3
M1 - 031504
ER -