Dynamic Fuzzy Pattern Matching for the monitoring of non stationary processes

In the domain of diagnosis by Pattern Recognition, a pattern is a simplified observation about the system state and each class, containing similar patterns, represents a functioning mode. Classes issued of non stationary processes are dynamic and their characteristics vary over the time. In this paper, the classification method Incremental Fuzzy Pattern Matching (IFPM) is developed for the monitoring of non stationary processes. This development is based on the monitoring of the accumulative changes in the data distribution. When these changes reach a threshold prefixed by an expert, the data distribution will be recursively updated online using the recent and useful patterns.