Deep Neural Networks Analysis of Borescope Images
Open accessAbstract
This paper presents results on applying deep neural networks to analysis of images from borescope inspections of large turbofan engines, carried out in the field. Such inspections are done as a part of routine monitoring and maintenance as well as an initial, investigative response to alerts from automatic monitoring systems, pilots or engineers. Across GE’s commercial engines fleet, a substantial number of images have been gathered this way. The deep learning techniques that have come out of computer vision and machine learning research in the last decade offer new possibilities for analyzing and mining such data collections. This paper presents initial results on separating borescope images from images created with regular digital cameras, as well as classifying images containing various engine parts, with average accuracy of 95% and 77%, respectively, on unseen validation data.
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There is a lack of recent applications of classical image processing techniques to blade inspection.
“There is a lack of recent applications of these techniques to blade inspection. In fact, the field is somewhat weak and has been dominated by the neural networks and deep learning approaches instead [9,19,47,85].”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [85]
The Abstract describes this paper’s use of deep neural networks for borescope images, and the Conclusion and Discussion mentions another neural-network application to turbine-blade images. Neither passage assesses how many recent blade-inspection studies use classical image processing. Searches for classical and traditional image-processing approaches found no such assessment in our copy. (LLM, conf=0.73)
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- Authors
- Svensen, M., Powrie, H. E. G., & Hardwick, D. S.
- ORCID
- Powrie, Honor E G
- Venue
- PHM Society European Conference
- Publisher
- PHM Society
- Published
- 2018-06-30
- Volume
- 4
- Issue
- 1
- ISSN
- 2325-016X (ISSN-L)
- OpenAlex
- W2886713210
- Cited by
- 17
- References
- 12
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 2 · 2022: 7 · 2021: 1 · 2020: 2
- FWCI
- 1.13
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- PHM Society European Conference (publishedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Keywords
- turbofan engine, deep neural network, image classification, aircraft engine maintenance, condition monitoring, image data mining
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