导航

ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2018, Vol. 39 ›› Issue (3): 221706-221706.doi: 10.752/S1000-6893.2017.21706

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

Dynamic recognition method for reliability of general aviation fleet equipment

CHEN Yonggang, XIONG Shenghua, HE Qiang, HE Yuanhua   

  1. College of Civil Aviation Safety Engineering, Civil Aviation Flight University of China, Guanghan 618307, China
  • Received:2017-08-29 Revised:2017-10-27 Online:2018-03-15 Published:2018-04-10
  • Supported by:
    National Natural Science Foundation of China (U1633203); CAAC Scientific Research Base on Aviation Flight Technology and Safety (F2015KF01); Civil Aviation Flight University of China Scientific Research Foundation (J2014-32)

Abstract: Reliability of general aviation fleet equipment is a precondition for safe operation of the airline company, and is one of the important factors that affect the economic benefits of the company. According to the statistics, analysis and actual usage of reliability data of general aviation fleet equipment, the index system is constructed according to the ATA100 chapter name. Based on the advantages of the variable fuzzy recognition method and the weight ladder naive Bayesian classifier model, a dynamic recognition model for reliability of general aviation fleet equipment is constructed. To avoid the irrationality caused by the subjective given index weight, the entropy weight method is used to obtain the index weight objectively. An example is used to verify the rationality of the weight ladder naive Bayesian classifier model, and reliability recognition of the identified sample is carried out based on the proposed method. Analysis of the example shows that the weight ladder Bayesian classifier reliability recognition model for general aviation fleet equipment is feasible and reasonable, providing a scientific method for reliability recognition of general aviation fleet equipment.

Key words: general aviation, aircraft maintenance, fuzzy recognition, entropy weight method, naive Bayesian classifier

CLC Number: