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Automated Condition Evaluation of Hot-Gas Path Components of Jet Engines Through Exhaust Jet Analysis

[+] Author Affiliations
Ulrich Hartmann, Joerg R. Seume

Leibniz Universität Hannover, Hannover, Germany

Paper No. GT2018-75384, pp. V006T05A011; 11 pages
  • ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition
  • Volume 6: Ceramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy
  • Oslo, Norway, June 11–15, 2018
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-5112-8
  • Copyright © 2018 by ASME


This paper determines the influence of different defective components in the hot-gas path (HGP) of a civil aircraft engine on the density distribution in the exhaust. The intention is to automate the identification of defective components inside the HGP through an analysis of the density distribution in the exhaust jet. The defects include an increased radial gap of the blades in the high-pressure turbine (HPT), and a reduction of the film cooling air mass flow in the first stage of the HPT. In addition, several combinations of both defects are simulated. In the present paper the exhaust density distributions are generated numerically using CFD simulations of the HGP. The density distribution in the exhaust jet is reconstructed with synthetic Background-Oriented Schlieren (BOS) measurements and automatically analyzed. The methodology for the automated defect detection consists of two algorithms, a Support Vector Machine (SVM) algorithm to automatically classify each measurement into a corresponding defect or reference class and an outlier detection algorithm to detect variations from the reference state — without assignment.

It is shown that BOS provides a sufficient reconstruction quality to automatically detect defective HGP components with a SVM algorithm. It is possible to automatically detect both defects, even when they occur at the same time. For this purpose, different features were calculated to isolate the influence of each defect on the density distribution. The outlier detection algorithm allows for an automated detection of variations in the density distribution compared to the reference state without any previous knowledge of the influence of the defects on the density distributions during the training procedure. With this algorithm it is possible to detect unknown or new defects which have not been observed or regarded yet. These results strengthen the hypothesis, that an automated detection of defects in jet engines prior to the disassembly is possible.

Copyright © 2018 by ASME



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