Evaluation of Influence Factors on the Visual Inspection Performance of Aircraft Engine Blades

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dc.contributor.authorAust J
dc.contributor.authorPons, Dirk
dc.contributor.authorMitrovic, Antonija
dc.date.accessioned2023-01-12T19:42:15Z
dc.date.available2023-01-12T19:42:15Z
dc.date.issued2021en
dc.date.updated2022-11-07T15:26:00Z
dc.description.abstractBackground—There are various influence factors that affect visual inspection of aircraft engine blades including type of inspection, defect type, severity level, blade perspective and background colour. The effect of those factors on the inspection performance was assessed. Method—The inspection accuracy of fifty industry practitioners was measured for 137 blade images, leading to N = 6850 observations. The data were statistically analysed to identify the significant factors. Subsequent evaluation of the eye tracking data provided additional insights into the inspection process. Results—Inspection accuracies in borescope inspections were significantly lower compared to piece-part inspection at 63.8% and 82.6%, respectively. Airfoil dents (19.0%), cracks (11.0%), and blockage (8.0%) were the most difficult defects to detect, while nicks (100.0%), tears (95.5%), and tip curls (89.0%) had the highest detection rates. The classification accuracy was lowest for airfoil dents (5.3%), burns (38.4%), and tears (44.9%), while coating loss (98.1%), nicks (90.0%), and blockage (87.5%) were most accurately classified. Defects of severity level S1 (72.0%) were more difficult to detect than increased severity levels S2 (92.8%) and S3 (99.0%). Moreover, visual perspectives perpendicular to the airfoil led to better inspection rates (up to 87.5%) than edge perspectives (51.0% to 66.5%). Background colour was not a significant factor. The eye tracking results of novices showed an unstructured search path, characterised by numerous fixations, leading to longer inspection times. Experts in contrast applied a systematic search strategy with focus on the edges, and showed a better defect discrimination ability. This observation was consistent across all stimuli, thus independent of the influence factors. Conclusions—Eye tracking identified the challenges of the inspection process and errors made. A revised inspection framework was proposed based on insights gained, and support the idea of an underlying mental model.en
dc.identifier.citationAust J, Pons D, Mitrovic A (2021). Evaluation of Influence Factors on the Visual Inspection Performance of Aircraft Engine Blades. Aerospace. 9(1). 18-18.en
dc.identifier.doihttp://doi.org/10.3390/aerospace9010018
dc.identifier.issn2226-4310
dc.identifier.urihttps://hdl.handle.net/10092/104974
dc.languageen
dc.language.isoenen
dc.publisherMDPI AGen
dc.rightsAll rights reserved unless otherwise stateden
dc.rights.urihttp://hdl.handle.net/10092/17651en
dc.subjectvisual inspectionen
dc.subjectinfluence factorsen
dc.subjectimpact factorsen
dc.subjectgas turbine engine bladesen
dc.subjectdefect detectionen
dc.subjecteye trackingen
dc.subjectMROen
dc.subjectaviationen
dc.subjectaircraft engine maintenanceen
dc.subject.anzsrcFields of Research::40 - Engineering::4001 - Aerospace engineering::400103 - Aircraft performance and flight control systemsen
dc.titleEvaluation of Influence Factors on the Visual Inspection Performance of Aircraft Engine Bladesen
dc.typeJournal Articleen
uc.collegeFaculty of Engineering
uc.departmentComputer Science and Software Engineering
uc.departmentMechanical Engineering
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