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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convolutional neural networks, image processing, deep learning
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