Risk factors analysis using the fuzzyfication of Reason's model

Modern technology has now reached a point where improved safety can only be achieved through a better understanding of human error mechanisms. Much of the theoretical structure have a particular importance is the identification of cognitive processes common to a wide variety of error types. The "Reason's model" helps to understand the causes of accidents and to highlight the complexity of cause and effect. This model examines the preconditions for the event. It offers a typology of human errors it introduces into context, the technical and organizational system. An essential element of the accident risk analysis is making numerous decisions. In this process expert rely on gained knowledge and experience. Lack of knowledge concerning the rules of logic can lead to dangerous errors and may result in continuous failures in performance flow from faulty reasoning processes. Since these effect factors especially human interference are characterized by uncertainty and imprecision, we proposed a tool for data analysis based on artificial intelligence techniques, including the principles of fuzzy logic. The result was very satisfactory. Program established for predicting the performance of a plot just from probably inputs variables of the system.