Research On Aviation Maintenance Human Error Analysis Based On Human Reliability

With the continuous development of modern science and technology of aviation industry, the safety and the reliability of the aircraft itself has improved continuously. Relevant data show that human factor has gradually become one of the main causes for aviation accidents. By analyzing the human errors in aviation maintenance system systematically, the causes of human errors can be found out and the error probability of maintenance personnel can be predicted effectively. The results of this research can provide scientific evidence for prevention and control measures and reduce accident rate which can improve aviation safety.Firstly, the basic knowledge and theories of aviation maintenance human error are introduced. Based on the REASON model, aviation maintenance human error analysis framework is set up which is applied to the investigation and analysis of maintenance accidents.Secondly, several typical human reliability analysis techniques are introduced and the cognitive reliability and error analysis method(CREAM) is selected as the basis method of this paper. According to the theory of CREAM tracing method and the practical situation of aviation maintenance system, the root cause analysis framework the aviation maintenance human error accident is constructed. Using the advantages of the decision making trial and evaluation laboratory(DEMATEL) method and the network process(ANP), the causes of aviation maintenance human errors are analyzed deeply. Then the weight coefficient of each influence factor is calculated so that the key factors that lead to aviation maintenance human error are determined.Finally, in the quantitative analysis of aviation maintenance human error probability, the aviation maintenance system has high complexity, fuzziness and uncertainty which can be solved by Bayesian network and fuzzy logic method effectively. Based on the CREAM prediction method, two kinds of improved error probability prediction method are established which can be used to predict the aviation maintenance error probability. The results of instance calculation and analysis show that the improved methods can effectively solve the fuzziness and uncertainty problems in the process of common performance condition level evaluation and cognitive control mode recognition. The methods can also reduce the subjectivity of expert evaluation and improve the prediction accuracy of human error probability which can provide decision support for the risk control of aviation maintenance system.