Stream data clustering for engineering applications a use case of autonomous guided vehicles

The article presents the results of a study to verify the possibility of discovering the type of work performed by a monitored object. During the research, the monitored object was an AGV streaming data about its current state. Each value representing the state of the AGV was transmitted in a separate stream. The data transmitted could be at different frequencies for each stream. The goal was to verify the possibility of discovering the type of work performed by the AGV on the basis of data that was generated only by the monitored object (without data from external systems). In the course of the work, a mechanism was developed to identify the beginning and end of the work performed by the AGV, as well as a way to aggregate the values characterizing the work performed. The set of characteristics of the work was selected in a manner that allowed easy interpretation by AGV fleet managers. Discovery of the type of work performed was done using two clustering algorithms: KMeans++ and DBScan. The set of features analyzed by the algorithms was selected experimentally. The results obtained with the two algorithms were compared. The identified work types were used to create work profiles, characterized by feature sets and appropriate value ranges.