Human Reliability Analysis for Visual Inspection in Aviation Maintenance by a Bayesian Network Approach

Abstract

Visual inspection plays an important role in aviation maintenance. Human reliability analysis (HRA) in this field is necessary and can bring benefits to better human error management. Because of the lack of safety data, the present paper aims to introduce the Bayesian network (BN) approach to perform HRA in visual inspection, which permits the utilization of multi-disciplinary sources of objective and subjective information. In this paper, significant influence factors of visual inspection are identified according to the Human Factors Analysis and Classification System–Maintenance Extension. Then a network representing the visual inspection performance model is constructed. Expert opinions, data fusion from accident reports, and related literature are utilized in the step of obtaining parameters. Two canonical models used in probabilistic network model building, the Noisy-OR gates and the Recursive Noisy-OR rule, are applied to generate conditional probabilities from parameters obtained by the absolute probability judgment technique. Through the BN inference, the inspection reliability can be assessed, and some conclusions and recommendations are drawn that could provide theoretical base and data support to make interventions for safety management of visual inspection.