Application for manufacturing systems served by collaborative robots monitoring

Abstract

The article proposes an improvement tool, Petri Nets type, dedicated to hierarchical recurrent systems modeling. This tool can be implemented in manufacturing systems served by collaborative robots if the recurrent modeling function is structured on hierarchical levels. Each level is similar with previous ones, from superior hierarchical level, but has allocated different time intervals. In low hierarchical levels, those intervals are bigger, decreasing detection accuracy. Hierarchical levels are interfaced by LIFO module and OOPN (Object Oriented Petri Nets). This new approach base on temporal hierarchical structure, refined monitoring function modeling. On each horizontal level is proposed an elementary detection model, but transitions are associated with external events synchronized with fault occurrence, expected specific temporal windows. Not all the external events may occur in the assigned intervals so the model is designed to evolve to the next level, and to preserve / transferring its set of detected external events. The model evolution is made on every horizontal level, until the node where detection is finished, and moves to the next level.