Authors: Jelena Djurovic, Danica Brzic, Tatjana Kaludjerovic Radoicic
Abstract: In this paper event tree model as qualitative support method was used. This is applied to fault tree analysis. Transfer and transformation facts and rules for problem solving were described. Method for knowledge extraction was presented. Mechanism of decision making and conclusions was implemented. Faults detection and classification were examined. That is significant for isolating all types of hazard and take appropriate steps to reduce and control these hazards.
Keywords: Fault Tree, Cause-Consequence, Classification, Rules Modeling, Acquisition
If it goes back to the chief tasks of safety analysis methods can be placed in four groups, two relating to the identification of problems and two to their assessment. All formal methods can be considered as combinations of two methods, cause-consequence analysis, a combination of incident sequence analysis and fault tree analysis -.
In the design of a new plant, they should come in at the detailed safety design phase, when most of the Piping and instrumentation diagram (PID) are ready -. These methods are also useful in the inspection of plants already built and on stream. The methods from this class incident sequence analysis and fault tree analysis examine links between faults, represent them graphically, and can assign probabilities to them. Incident sequence analysis starts with a single fault, and observes how it may develop. The working hypothesis is that every safety measure can succeed or fail with a certain probability.
The design and construction of safe plants calls for a highly structured and organized procedure clearly setting forth what has to be done by whom and in what way, and focusing on the creation and routing of documents. This is called a safety management system.
Knowledge bases can be understand as a special case decision support systems and the other hand knowledge bases present new stage in development evaluation step of information technologies. Knowledge base is the basic element of the expert system. Expression expert system is very often applied to program which use knowledge for behavior man-expert simulation, or whose function has some attributes man–expert behavior , . It has power to learn from experience, general knowledge achievement, reconceptualization, analogy resonable, transfer knowledge from one domain to the other, flexibility and changeable approach for problem solution. Behind these, expert selecting alternative solutions, explaining its diagnosis as well as to learn from previously experiences adding knowledge base new elements which achieving during the problem solution ,,.
Basic difference between expert systems and classical programs is that expert systems manipulate with knowledge and classical programs manipulate with data. Expert system has ability to solve complex problem which including uncertainty by information processing.
In this paper knowledge building for problem recognition and solving was developed.
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