Background—The visual inspection of aircraft parts such as engine blades is crucial to ensure safe aircraft operation. There is a need to understand the reliability of such inspections and the factors that affect the results. In this study, the factor ‘cleanliness’ was analysed among other factors. Method—Fifty industry practitioners of three expertise levels inspected 24 images of parts with a variety of defects in clean and dirty conditions, resulting in a total of N = 1200 observations. The data were analysed statistically to evaluate the relationships between cleanliness and inspection performance. Eye tracking was applied to understand the search strategies of different levels of expertise for various part conditions. Results—The results show an inspection accuracy of 66.8% and 86.8% for clean and dirty blades, respectively. The statistical analysis showed that cleanliness and defect type influenced the inspection accuracy, while expertise was surprisingly not a significant factor. In contrast, inspection time was affected by expertise along with other factors, including cleanliness, defect type and visual acuity. Eye tracking revealed that inspectors (experts) apply a more structured and systematic search with less fixations and revisits compared to other groups. Conclusions—Cleaning prior to inspection leads to better results. Eye tracking revealed that inspectors used an underlying search strategy characterised by edge detection and differentiation between surface deposits and other types of damage, which contributed to better performance.

Context—The maintenance of aero engines is intricate, time-consuming, costly and has significant functional and safety implications. Engine blades and vanes are the most rejected parts during engine maintenance. Consequently, there is an ongoing need for more effective and efficient inspection processes. Purpose—This paper defines engine blade defects, assigns root-causes, shows causal links and cascade effects and provides a taxonomy system. Approach—Defect types were identified from the literature and maintenance manuals, categorisations were devised and an ontology was created. Results—Defect was categorised into Surface Damage, Wear, Material Separation and Material Deformation. A second categorisation identified potential causes of Impact, Environmental causes, Operational causes, Poor maintenance, Poor manufacturing and Fatigue. These two categorisations were integrated with an ontology. Originality—The work provides a single comprehensive illustrated list of engine blade defects, and a standardised defect terminology, which currently does not exist in the aviation industry. It proposes a taxonomy for both engine blade defects and root-causes, and shows that these may be related using an ontology.

Background—In aircraft engine maintenance, the majority of parts, including engine blades, are inspected visually for any damage to ensure a safe operation. While this process is called visual inspection, there are other human senses encompassed in this process such as tactile perception. Thus, there is a need to better understand the effect of the tactile component on visual inspection performance and whether this effect is consistent for different defect types and expertise groups. Method—This study comprised three experiments, each designed to test different levels of visual and tactile abilities. In each experiment, six industry practitioners of three expertise groups inspected the same sample of N = 26 blades. A two-week interval was allowed between the experiments. Inspection performance was measured in terms of inspection accuracy, inspection time, and defect classification accuracy. Results—The results showed that unrestrained vision and the addition of tactile perception led to higher inspection accuracies of 76.9% and 84.0%, respectively, compared to screen-based inspection with 70.5% accuracy. An improvement was also noted in classification accuracy, as 39.1%, 67.5%, and 79.4% of defects were correctly classified in screen-based, full vision and visual–tactile inspection, respectively. The shortest inspection time was measured for screen-based inspection (18.134 s) followed by visual–tactile (22.140 s) and full vision (25.064 s). Dents benefited the most from the tactile sense, while the false positive rate remained unchanged across all experiments. Nicks and dents were the most difficult to detect and classify and were often confused by operators. Conclusions—Visual inspection in combination with tactile perception led to better performance in inspecting engine blades than visual inspection alone. This has implications for industrial training programmes for fault detection.

Background—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.

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.