Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection

Aust, Jonas and Shankland, Sam and Pons, Dirk and Mukundan, Ramakrishnan and Mitrovic, Antonija (2021) Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection. Aerospace, 8 (2). p. 30. ISSN 2226-4310 (In Press)

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Abstract

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.

Item Type: Article
Uncontrolled 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
Subjects: STM Repository > Engineering
Depositing User: Managing Editor
Date Deposited: 21 Oct 2024 04:11
Last Modified: 21 Oct 2024 04:11
URI: http://classical.goforpromo.com/id/eprint/1389

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