University of Canterbury Home
    • Admin
    UC Research Repository
    UC Library
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    1. UC Home
    2. Library
    3. UC Research Repository
    4. College of Engineering
    5. Engineering: Journal Articles
    6. View Item
    1. UC Home
    2.  > 
    3. Library
    4.  > 
    5. UC Research Repository
    6.  > 
    7. College of Engineering
    8.  > 
    9. Engineering: Journal Articles
    10.  > 
    11. View Item

    Automated defect detection and decision-support in gas turbine blade inspection (2021)

    Thumbnail
    View/Open
    Published version (8.372Mb)
    Type of Content
    Journal Article
    UC Permalink
    https://hdl.handle.net/10092/101783
    
    Publisher's DOI/URI
    http://doi.org/10.3390/aerospace8020030
    
    Publisher
    MDPI AG
    ISSN
    2226-4310
    Language
    en
    Collections
    • Engineering: Journal Articles [1330]
    Authors
    Aust, Jonas cc
    Shankland, Sam
    Pons, Dirk cc
    Mukundan, Ramakrishnan cc
    Mitrovic, A.
    show all
    Abstract

    © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Background—In the field of aviation, maintenance and inspections of engines are vitally important in ensuring the safe functionality of fault-free aircrafts. There is value in exploring automated defect detection systems that can assist in this process. Existing effort has mostly been directed at artificial intelligence, specifically neural networks. However, that approach is critically dependent on large datasets, which can be problematic to obtain. For more specialised cases where data are sparse, the image processing techniques have potential, but this is poorly represented in the literature. Aim—This research sought to develop methods (a) to automatically detect defects on the edges of engine blades (nicks, dents and tears) and (b) to support the decision-making of the inspector when providing a recommended maintenance action based on the engine manual. Findings—For a small sample test size of 60 blades, the combined system was able to detect and locate the defects with an accuracy of 83%. It quantified morphological features of defect size and location. False positive and false negative rates were 46% and 17% respectively based on ground truth. Originality—The work shows that image-processing approaches have potential value as a method for detecting defects in small data sets. The work also identifies which viewing perspectives are more favourable for automated detection, namely, those that are perpendicular to the blade surface.

    Citation
    Aust J, Shankland S, Pons D, Mukundan R, Mitrovic A (2021). Automated defect detection and decision-support in gas turbine blade inspection. Aerospace. 8(2). 1-27.
    This citation is automatically generated and may be unreliable. Use as a guide only.
    Keywords
    automated defect detection; blade inspection; gas turbine engines; aircraft; visual inspection; image segmentation; image processing; applied computing; computer vision; object detection; maintenance automation; aerospace; MRO
    ANZSRC Fields of Research
    09 - Engineering::0901 - Aerospace Engineering::090103 - Aerospace Structures
    09 - Engineering::0901 - Aerospace Engineering::090104 - Aircraft Performance and Flight Control Systems
    09 - Engineering::0915 - Interdisciplinary Engineering::091507 - Risk Engineering (excl. Earthquake Engineering)
    09 - Engineering::0902 - Automotive Engineering::090204 - Automotive Safety Engineering
    Rights
    All rights reserved unless otherwise stated
    http://hdl.handle.net/10092/17651
    Advanced Search

    Browse

    All of the RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThesis DisciplineThis CollectionBy Issue DateAuthorsTitlesSubjectsThesis Discipline

    Statistics

    View Usage Statistics
    • SUBMISSIONS
    • Research Outputs
    • UC Theses
    • CONTACTS
    • Send Feedback
    • +64 3 369 3853
    • ucresearchrepository@canterbury.ac.nz
    • ABOUT
    • UC Research Repository Guide
    • Copyright and Disclaimer
    • SUBMISSIONS
    • Research Outputs
    • UC Theses
    • CONTACTS
    • Send Feedback
    • +64 3 369 3853
    • ucresearchrepository@canterbury.ac.nz
    • ABOUT
    • UC Research Repository Guide
    • Copyright and Disclaimer