A flexible quantitative values definition method for stochastic qualitative reasoning

Abstract

For fault detection of a complex system such as an air-conditioning system, we have developed an effective stochastic qualitative reasoning method. In this method, a complex system is expressed by its simple qualitative models. By comparing the states derived from the reasoning with the observations, the “agreement rate”, which is a parameter that shows how the reasoning reflects the real world, can be calculated. The malfunctioning parts of a system can be identified by the highest “agreement rate”.
In the stochastic qualitative reasoning, we have used a landmark separating definition for changing quantitative data into qualitative values. According to this definition, a little change in the quantitative data often gives rise to the radical fluctuation in the corresponding qualitative values. To tackle this problem, we propose a flexible definition using fuzzy set. In this flexible definition, the quantitative data near the landmark have double-defined qualitative values, and the grade of the quantitative data belonging to each qualitative value is expressed by the membership function.
This flexible definition was applied to a heat source system of a heat source system, and its effectiveness has been confirmed.