Adaptive and Predictive Diagnosis Based on Pattern Recognition

Systems work in either normal or abnormal functioning modes. Pattern Recognition (PR) is a set of methods used to classify a pattern into one of a set of predefined classes. Each class is associated to a functioning mode. If the pattern is considered as the observation of the actual functioning mode, then the diagnosis by PR is realized by deciding the class of the actual pattern. PR is particularly adapted to realize the diagnosis when the prior information about system functioning modes is not sufficient to construct an analytical or structural model of the system functioning. However this knowledge is often incomplete because it cannot contain information about all system functioning modes. Thus the diagnosis method must be adaptive to include into its database all the new functioning modes. In addition, the diagnosis must be predictive in order to follow the evolution of the system from one mode to another one. We use the supervised classification method Fuzzy Pattern Matching (FPM) to realize the diagnosis of non-evolutionary systems with a complete database. Indeed, FPM cannot realize the adaptive and predictive diagnosis. Therefore, a solution to this problem is proposed in this paper. The performance of the proposed approach is illustrated using different examples.