Fuzzy reasoning in co-operative supervision systems

International audienceThis paper considers a decision support system dedicated to fault detection and isolation from a human–machine co-operation point of view. Detection and isolation are based on different models of the process (non-linear and linear causal local models). Reasoning using real numbers is often used by human beings; fuzzy logic is introduced as a numerical-symbolic interface between the quantitative fault indicators and the symbolic diagnostic reasoning on them; it also provides an effective decision-making tool in imprecise or uncertain environments while managing model uncertainty, sensor imprecision and vague normal behavior limits. Fuzzy rules are modelled geometrically; fuzzy sets are represented as points in a description space. A prototype graphical interface with structural, causal and historical views gives complete information to the human operator. In such an interface, fuzziness is displayed as a colour palette evolving with time