TY - GEN

T1 - Deep Learning Based Crack Detection in Inhomogeneous X-Ray Images for High Pressure Turbine Blades in Aviation

AU - Kuhlgatz, Timo

AU - Ihler, Sontje

AU - Bonhage, Marius

AU - Seel, Thomas

N1 - Publisher Copyright: © 2024 by ASME.

PY - 2024/8/28

Y1 - 2024/8/28

N2 - To ensure safety in aviation, there are stringent requirements for aircraft components, not only in design and manufacturing but also their maintenance. For economic reasons, the maintenance process is intended to be as efficient as possible while still meeting required safety standards. The increase in automation level contributes to enhancing the economic efficiency of the maintenance process. A highly time-consuming step involves the manual assessment of X-ray images. Advances in deep learning algorithms suggest a promising prospect for employing these algorithms to automatically inspect X-ray images. This paper introduces a deep learning-based approach to enhance the level of automation in the inspection of X-ray images of high-pressure turbine blades by automatically detecting the presence of internal axial cracks. We encountered challenges posed by the heterogeneous X-ray images with very small and variable damage patterns, along with the issue of limited available data. To address these challenges, we applied damage mapping to define a region of interest and used the concept of transfer learning. Three different model architectures of deep neural networks (Resnet18, Inception-V3 and Densenet161) were compared, and it is demonstrated that all three are well-suited (average AUPRC > 0.95 and average F2-Score > 0.93) for our task. We are therefore able to help increase the degree of automation of the HPT blade X-rays’ inspection process and thus make the maintenance process more efficient.

AB - To ensure safety in aviation, there are stringent requirements for aircraft components, not only in design and manufacturing but also their maintenance. For economic reasons, the maintenance process is intended to be as efficient as possible while still meeting required safety standards. The increase in automation level contributes to enhancing the economic efficiency of the maintenance process. A highly time-consuming step involves the manual assessment of X-ray images. Advances in deep learning algorithms suggest a promising prospect for employing these algorithms to automatically inspect X-ray images. This paper introduces a deep learning-based approach to enhance the level of automation in the inspection of X-ray images of high-pressure turbine blades by automatically detecting the presence of internal axial cracks. We encountered challenges posed by the heterogeneous X-ray images with very small and variable damage patterns, along with the issue of limited available data. To address these challenges, we applied damage mapping to define a region of interest and used the concept of transfer learning. Three different model architectures of deep neural networks (Resnet18, Inception-V3 and Densenet161) were compared, and it is demonstrated that all three are well-suited (average AUPRC > 0.95 and average F2-Score > 0.93) for our task. We are therefore able to help increase the degree of automation of the HPT blade X-rays’ inspection process and thus make the maintenance process more efficient.

KW - Deep Learning

KW - crack detection

KW - X-ray inspection

KW - diagnostics

KW - classification

KW - deep learning

KW - damage detection

KW - limited data

KW - automated X-ray inspection

KW - inhomogeneous X-ray images

UR - http://www.scopus.com/inward/record.url?scp=85204304346&partnerID=8YFLogxK

U2 - 10.1115/gt2024-123663

DO - 10.1115/gt2024-123663

M3 - Conference contribution

VL - 4

T3 - Proceedings of the ASME Turbo Expo

BT - Controls, Diagnostics, and Instrumentation

ER -