A deep-learning-based approach for aircraft engine defect detection

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dc.contributor.authorUpadhyay, Anurag
dc.contributor.authorLi, Jun
dc.contributor.authorKing, Steve
dc.contributor.authorAddepalli, Sri
dc.date.accessioned2023-02-08T10:52:31Z
dc.date.available2023-02-08T10:52:31Z
dc.date.issued2023-02-01
dc.description.abstractBorescope inspection is a labour-intensive process used to find defects in aircraft engines that contain areas not visible during a general visual inspection. The outcome of the process largely depends on the judgment of the maintenance professionals who perform it. This research develops a novel deep learning framework for automated borescope inspection. In the framework, a customised U-Net architecture is developed to detect the defects on high-pressure compressor blades. Since motion blur is introduced in some images while the blades are rotated during the inspection, a hybrid motion deblurring method for image sharpening and denoising is applied to remove the effect based on classic computer vision techniques in combination with a customised GAN model. The framework also addresses the data imbalance, small size of the defects and data availability issues in part by testing different loss functions and generating synthetic images using a customised generative adversarial net (GAN) model, respectively. The results obtained from the implementation of the deep learning framework achieve precisions and recalls of over 90%. The hybrid model for motion deblurring results in a 10× improvement in image quality. However, the framework only achieves modest success with particular loss functions for very small sizes of defects. The future study will focus on very small defects detection and extend the deep learning framework to general borescope inspection.en_UK
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC): 113174en_UK
dc.identifier.citationUpadhyay A, Li J, King S, Addepalli S. (2023) A deep-learning-based approach for aircraft engine defect detection, Machines, Volume 11, Issue 2, February 2023, Article number 192en_UK
dc.identifier.issn2075-1702
dc.identifier.urihttps://doi.org/10.3390/machines11020192
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19148
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectborescope inspectionen_UK
dc.subjectimagesen_UK
dc.subjectmotion deblurringen_UK
dc.subjectU-Neten_UK
dc.subjectGANen_UK
dc.titleA deep-learning-based approach for aircraft engine defect detectionen_UK
dc.typeArticleen_UK

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