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