Symptoms detection in FMS through performance indicators follow-up: A quality-flow approach

In this paper, we are interested in the detection of symptoms in flexible manufacturing systems (FMS). The goal is to contribute to the setting up of a model that allows the detection of deficiency symptoms by the definition of correlation bonds between the parameters of the product quality, the material flow, and the state of the entities of the process. We assume that the cause of a drift is related to the failing state of a resource that implicates a decline in the product quality and/or flow before recognizing a collapse of its performances. This consequently results in a delay in the accomplishment of its function compared to the preset parameters. The principal contribution of this work is the improvement of the symptoms detection method based on the use of temporal object Petri nets (TOPN) tool, which allows the observation of the appearance of the deficiency symptoms as signalled by control sensors.