Details
| Original language | English |
|---|---|
| Title of host publication | 2023 Intermountain Engineering, Technology and Computing, IETC 2023 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 43-48 |
| Number of pages | 6 |
| ISBN (electronic) | 9798350335903 |
| ISBN (print) | 979-8-3503-3591-0 |
| Publication status | Published - 2023 |
| Event | 2023 Annual Intermountain Engineering, Technology and Computing, IETC 2023 - Provo, United States Duration: 12 May 2023 → 13 May 2023 |
Abstract
In this paper, the utility and accuracy of Machine Learning (ML) and Deep Learning (DL) methods are investigated for detecting defects in civil aircraft engines. Rather than to disassemble jet engines, the approach investigated in this study utilizes images of the exhaust of jet engines and infers defects in the turbine and burner section. While the proposed DL methods make use of one or two cameras, the ML methods depend on data obtained by extracting the density fields of the Hot Gas Path (HGP). The HPG data are computed from images acquired by an array of cameras. The corresponding ML features are crafted from these density fields. The proposed algorithms employ optimized hyperparameters and separate training as well as validation data sets. The study illustrates the potential of DL methods and the resulting simplification in the necessary instrumentation to accomplish near perfect defect classification outcomes.
Keywords
- Deep learning, defect classification, hot gas path, jet engines, machine learning
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Physics and Astronomy(all)
- Instrumentation
- Social Sciences(all)
- Education
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Software
- Engineering(all)
- Electrical and Electronic Engineering
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2023 Intermountain Engineering, Technology and Computing, IETC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 43-48.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Deep and Machine Learning-based Methods for Defect Classification in Jet Engines
AU - Schoen, Marco P.
AU - Oettinger, Marcel
AU - Mimic, Dajan
N1 - Funding Information: This research is based on data funded by the German Science Foundation (DFG) within the framework 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. We would also like to acknowledge the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany s Excellence Strategy EXC 2163/1 Sustainable and Energy Efficient Aviation Project ID 390881007.
PY - 2023
Y1 - 2023
N2 - In this paper, the utility and accuracy of Machine Learning (ML) and Deep Learning (DL) methods are investigated for detecting defects in civil aircraft engines. Rather than to disassemble jet engines, the approach investigated in this study utilizes images of the exhaust of jet engines and infers defects in the turbine and burner section. While the proposed DL methods make use of one or two cameras, the ML methods depend on data obtained by extracting the density fields of the Hot Gas Path (HGP). The HPG data are computed from images acquired by an array of cameras. The corresponding ML features are crafted from these density fields. The proposed algorithms employ optimized hyperparameters and separate training as well as validation data sets. The study illustrates the potential of DL methods and the resulting simplification in the necessary instrumentation to accomplish near perfect defect classification outcomes.
AB - In this paper, the utility and accuracy of Machine Learning (ML) and Deep Learning (DL) methods are investigated for detecting defects in civil aircraft engines. Rather than to disassemble jet engines, the approach investigated in this study utilizes images of the exhaust of jet engines and infers defects in the turbine and burner section. While the proposed DL methods make use of one or two cameras, the ML methods depend on data obtained by extracting the density fields of the Hot Gas Path (HGP). The HPG data are computed from images acquired by an array of cameras. The corresponding ML features are crafted from these density fields. The proposed algorithms employ optimized hyperparameters and separate training as well as validation data sets. The study illustrates the potential of DL methods and the resulting simplification in the necessary instrumentation to accomplish near perfect defect classification outcomes.
KW - Deep learning
KW - defect classification
KW - hot gas path
KW - jet engines
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85164539539&partnerID=8YFLogxK
U2 - 10.1109/IETC57902.2023.10152188
DO - 10.1109/IETC57902.2023.10152188
M3 - Conference contribution
AN - SCOPUS:85164539539
SN - 979-8-3503-3591-0
SP - 43
EP - 48
BT - 2023 Intermountain Engineering, Technology and Computing, IETC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 Annual Intermountain Engineering, Technology and Computing, IETC 2023
Y2 - 12 May 2023 through 13 May 2023
ER -