A New Approach using Characteristic Video Signals to Improve the Stability of Manufacturing Processes

Abstract

Observing production processes is a typical task for sensors in industrial environments. This paper deals with the use of camera systems as a sensor array to compare similar production processes with one another. The aim is to detect anomalies in production processes, such as the motion of robots or the flow of liquids. Since the comparison of high-resolution and long videos is very resource-intensive, we propose clustering the video into areas and shots. Therefore, we suggest interpreting each pixel of a video as a signal varying in time. In order to do that without any background knowledge and to be useful for any production environment with motion involved, we use an unsupervised clustering procedure. We show three different preprocessing approaches to avoid faulty clustering of static image areas and those relevant for the production and finally compare the results.