Industrial Energy Assessment Training Effectiveness Evaluation: An Eye-Tracking Study

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

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.

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

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 86.8% and 66.8% 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.