Compared with federated avionic architecture, the integrated modular avionic (IMA) system architecture in the aircraft can provide more sophisticated and powerful avionic functionality, and meanwhile, it becomes structurally dynamic, variably interconnected, and highly complex. The traditional approach such as fault tree analysis (FTA) becomes neither convenient nor sufficient in making safety analysis of the IMA system. In order to overcome the limitations, the approach that FTA combines with generalized stochastic petri net (GSPN) is proposed. First, FTA is used to establish the static model for the top level of the IMA system, while GSPN is used to build a dynamic model for each cell system. Finally, the combination model is generated, which is called the FTGPN model. Moreover, the FTGPN model is made safety analysis with the PIPE2 tool. According to the simulation result, corresponding measures are taken to meet the safety requirements of the IMA system.
Human error is an important risk factor for flight safety. Although the human error assessment and reduction technique (HEART) is an available tool for human reliability derivation, it has not been applied in flight safety assessment. The traditional HEART suffers from imprecise calculation of the assessed proportion of affect (APOA) because it heavily depends on a single expert's judgment. It also fails to provide remedial measures for flight safety problems. To overcome these defects of the HEART, this study proposes an integrated human error quantification approach that uses the improved analytic hierarchy process method to determine the APOA values. Then, these values are fused to the HEART method to derive the human error probability. A certain flight task is completed to assess human reliability. The results demonstrate that the proposed method is a reasonable and feasible tool for quantifying human error probability and assessing flight safety in the aircraft manipulation process. In addition, the critical error-producing conditions influencing flight safety are identified, and improvement measures for high-error-rate operations are provided. The proposed method is useful for reducing the possibility of human error and enhancing flight safety levels in aircraft operation processes.
Human error is one of the most important risk factors affecting aviation safety. The original Cognitive Reliability and Error Analysis Method (CREAM) developed for the nuclear industry is reliable for human reliability quantification, but it is not fully applicable to human reliability analysis in aviation because it neglects the characteristics of long-duration flights. Here, we propose a modified CREAM method to predict human error probability in flight and provide some improvement measures for critical operations. A set of performance influencing factors (PIFs), such as flight procedures and ground support, is established to reflect operational scenarios in flight. Then, we develop the expected affect index of PIFs and the Scenario Influence Index to construct a quantitative model of human reliability. The probability of human error for each operation in the approach and landing phases is obtained with the modified CREAM method, and the results indicate that the most important cognitive function that influences human reliability is missed action. The proposed method may be a suitable tool for human reliability quantification in aviation considering long-duration flights. The method also has great practical significance for improving flight safety.
In consideration of the situation that civil aviation accidents involve many human-error factors and show the features of typical grey systems, an index system of civil aviation accident human-error factors is built using human factor analysis and classification system model. With the data of accidents happened worldwide between 2008 and 2011, the correlation between human-error factors can be analyzed quantitatively using the method of grey relational analysis. Research results show that the order of main factors affecting pilot human-error factors is preconditions for unsafe acts, unsafe supervision, organization and unsafe acts. The factor related most closely with second-level indexes and pilot human-error factors is the physical/mental limitations of pilots, followed by supervisory violations. The relevancy between the first-level indexes and the corresponding second-level indexes and the relevancy between second-level indexes can also be analyzed quantitatively.
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