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Acta Aeronautica et Astronautica Sinica ›› 2023, Vol. 44 ›› Issue (16): 228051-228051.doi: 10.7527/S1000-6893.2022.28051

• Solid Mechanics and Vehicle Conceptual Design • Previous Articles     Next Articles

Human factor reliability prediction model for civil aircraft maintenance task analysis

Qing GUO(), Deming GUAN   

  1. College of Aeronautical Engineering,Civil Aviation University of China,Tianjin 300300,China
  • Received:2022-09-27 Revised:2022-11-09 Accepted:2022-11-29 Online:2022-12-08 Published:2022-12-06
  • Contact: Qing GUO E-mail:qguocauc@sina.com
  • Supported by:
    Fundamental Research Funds for the Central Universities Civil Aviation University of China(3122021049)

Abstract:

Human factors as an important link in the human-machine relationship are currently becoming a key factor affecting civil aviation safety. Quantitative analysis of human factor reliability level in aircraft operations is important in reducing unconscious errors and ensuring flight safety. In this study, a combined THERP+CREAM model is developed according to NASA recommendations, the THERP data table optimized for the characteristics of civil aviation, the cognitive control mode of the aircrew maintenance personnel determined using the combination of fuzzy Bayesian networks and CREAM, and the predicted value of THERP embedded into CREAM using the average weight factor to realize the combination of two generations of methods and improve the accuracy of aircrew human factor reliability prediction. Investigation into a maintenance team of an airline company shows that the proposed THERP+CREAM prediction model can better predict the level of airframe human factor reliability, and realize that the human factor reliability analysis can be performed based on the maintenance tasks in the maintenance manual, hence providing a quantitative assessment method for the human factor analysis in the maintenance task analysis of the development unit.

Key words: civil aircraft maintenance, maintenance task analysis, human factor reliability, THERP, CREAM, Bayesian networks

CLC Number: