Fatigue Study Of Cessna 172 Maintenance Personnel Based On Eye Movement And ECG

With the use of a large number of Cessna 172 primary training aircraft in the field of general aviation,the pilot training unit and general aviation airport inspection workload surge,inspection is an important means to ensure the continuous airworthiness of the aircraft,and fatigue in the inspection will not only reduce the efficiency of maintenance personnel in the inspection work.Moreover,it is one of the important factors to induce maintenance personnel’s forget and mistakes,leading to navigation accident signs and even accidents.In view of the increasingly severe fatigue problem of maintenance personnel in the process of regular inspection,the study of maintenance personnel fatigue with the combination of qualitative and quantitative methods is not only helpful to reduce maintenance errors and improve maintenance efficiency,but also can improve the scientific nature of maintenance work plan.The main research results are as follows:1.Based on Functional Resonance Analysis Method(FRAM),the causes of maintenance errors in inspection are analyzed and 54 maintenance error prevention measures are proposed according to 24 single factor maintenance error causes analyzed.Based on the improved Systems-Theoretic Process Analysis(STPA),the fatigue causes in scheduled maintenance performance were analyzed and 11 fatigue management specifications were developed according to 90 kinds of fatigue causes.2.Combining NASA-TLX scale and Chalder Fatigue Scale as subjective fatigue test scale for maintenance personnel;The objective fatigue index of maintenance personnel was combined with eye movement and ECG index.Eleven fatigue indicators were screened out by statistical analysis,correlation analysis and normal-fatigue group comparison analysis,and a fatigue determination model was constructed based on Learning Vector Quantization(LVQ)and BP neural network.The results show that the qualitative research method based on FRAM and STPA provides a guarantee for determining the control and management of maintenance errors caused by maintenance personnel fatigue.Based on LVQ and BP neural network,the fatigue judgment accuracy of maintenance personnel in the inspection is 91.67% and 83.33% respectively,which has good effectiveness and robustness,and provides theoretical and methodological support for maintenance personnel fatigue research in the inspection.