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

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.

Assessment of the Effect of Cleanliness on the Visual Inspection of Aircraft Engine Blades: An Eye Tracking Study

“…There is a need to support human decision making during inspection [ 100 , 158 , 159 ]. The challenge is integrating human operators and inspection software.…”

“…Subsequently, a second set of images was taken of the now cleaned blades with the same camera setup and settings. For the image acquisition, we used the same set-up as in [ 100 ]. This comprised a self-made light tent and three LED ring-lights (LSY 6W manufactured by Superlux, Auckland, New Zealand) placed on the left and right side and on the top of the light tent for optimal illumination.…”

Comparison of Visual and Visual–Tactile Inspection of Aircraft Engine Blades

“…In the first task (screenbased inspection), images of the blades were shown to the participants. Those images were taken in a self-developed light tent with surround lighting as per [22]. In the full vision and visual-tactile inspection tasks, the actual parts were presented and handed out, respectively, to the participants.…”

Methodology for Evaluating Risk of Visual Inspection Tasks of Aircraft Engine Blades

“…The verification was done by using a software that measures the defect size visible on the photograph (in pixels) for each view [93]. The results were than compared and a ranking was made based on the visible defect size.…”