Dynamic K-Nearest Neighbors for the monitoring of evolving systems

In this article, a new Pattern Recognition (PR) approach is proposed to monitor the functioning modes evolutions in dynamic systems. When a functioning mode evolves, the system characteristics change and the observations, i.e. the patterns, obtained on the system change too. In this case, classes representing the system functioning modes have to be updated by keeping representative patterns only. The developed PR approach is based on the K-Nearest Neighbors (KNN) method. It is named Dynamic KNN (DKNN) and comprises two phases: a detection phase to detect and confirm classes evolutions and an adaptation phase realized incrementally to update the evolved classes parameters and reduce the dataset. To illustrate this approach, the monitoring of weldings quality (good or bad) is realized on an industrial system, based on acoustic noises issued of weldings operations.