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

Evaluation of expert skills in refinery patrol inspection: visual attention and head positioning behavior

We aimed to clarify expert skills in refinery patrol inspection using data collected through a virtual reality experimental system. As body positioning and postural changes are relevant factors during refinery patrol inspection tasks, we measured and analyzed both visual attention and head positioning behavior among experts and "knowledgeable novices" who were engaged in the engineering of the refinery but had less inspection experience. The participants performed a simulated inspection task, and the results showed that 1) expert inspectors could find more defects compared to knowledgeable novices, 2) visual attention behavior was similar between knowledgeable novices and experts, and 3) experts tended to position their heads at various heights and further from the inspection target to obtain visual information more effectively from the target compared to knowledgeable novices. This study presented the differences in head positioning behavior between expert and novice inspectors for the first time. These results suggest that to evaluate the skills used in inspecting relatively larger targets, both visual attention and head positioning behavior of the inspectors must be measured.

Assessment of Aircraft Engine Blade Inspection Performance Using Attribute Agreement Analysis

Background—Visual inspection is an important element of aircraft engine maintenance to assure flight safety. Predominantly performed by human operators, those maintenance activities are prone to human error. While false negatives imply a risk to aviation safety, false positives can lead to increased maintenance cost. The aim of the present study was to evaluate the human performance in visual inspection of aero engine blades, specifically the operators’ consistency, accuracy, and reproducibility, as well as the system reliability. Methods—Photographs of 26 blades were presented to 50 industry practitioners of three skill levels to assess their performance. Each image was shown to each operator twice in random order, leading to N = 2600 observations. The data were statistically analysed using Attribute Agreement Analysis (AAA) and Kappa analysis. Results—The results show that operators were on average 82.5% consistent with their serviceability decision, while achieving an inspection accuracy of 67.7%. The operators’ reproducibility was 15.4%, as was the accuracy of all operators with the ground truth. Subsequently, the false-positive and false-negative rates were analysed separately to the overall inspection accuracy, showing that 20 operators (40%) achieved acceptable performances, thus meeting the required standard. Conclusions—In aviation maintenance the false-negative rate of <5% as per Aerospace Standard AS13100 is arguably the single most important metric since it determines the safety outcomes. The results of this study show acceptable false-negative performance in 60% of appraisers. Thus, there is the desirability to seek ways to improve the performance. Some suggestions are given in this regard.

Comparative Analysis of Human Operators and Advanced Technologies in the Visual Inspection of Aero Engine Blades

Background—Aircraft inspection is crucial for safe flight operations and is predominantly performed by human operators, who are unreliable, inconsistent, subjective, and prone to err. Thus, advanced technologies offer the potential to overcome those limitations and improve inspection quality. Method—This paper compares the performance of human operators with image processing, artificial intelligence software and 3D scanning for different types of inspection. The results were statistically analysed in terms of inspection accuracy, consistency and time. Additionally, other factors relevant to operations were assessed using a SWOT and weighted factor analysis. Results—The results show that operators’ performance in screen-based inspection tasks was superior to inspection software due to their strong cognitive abilities, decision-making capabilities, versatility and adaptability to changing conditions. In part-based inspection however, 3D scanning outperformed the operator while being significantly slower. Overall, the strength of technological systems lies in their consistency, availability and unbiasedness. Conclusions—The performance of inspection software should improve to be reliably used in blade inspection. While 3D scanning showed the best results, it is not always technically feasible (e.g., in a borescope inspection) nor economically viable. This work provides a list of evaluation criteria beyond solely inspection performance that could be considered when comparing different inspection systems.

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.

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

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.