Semisupervised dynamic fuzzy K-nearest neighbors

Abstract

This chapter presents a semi-supervised dynamic classification method to deal with the problem of diagnosis of industrial evolving systems. Indeed, when a functioning mode evolves, the system characteristics change and the observations, i.e. the patterns representing observations in the feature space, obtained on the system change too. Thus, each class membership function must be adapted to take into account these temporal changes and to keep representative patterns only. This requires an adaptive method with a mechanism for adjusting its parameters over time. The developed approach is named Semi-Supervised Dynamic Fuzzy K-Nearest Neighbors (SS-DFKNN) and comprises three phases: a detection phase to detect and confirm classes evolutions, an adaptation phase realized incrementally to update the evolved classes parameters and to create new classes if necessary and a validation phase to keep useful classes only. To illustrate this approach, the diagnosis of a welding system is realized to detect the weldings quality (good or bad), based on acoustic noises issued of weldings operations.