Virtual Stochastic Sensors for Reconstructing Job Shop Production Workflows

Virtual stochastic sensors (VSS) can reconstruct the system behavior of partially observable systems that may contain concurrent non-Markovian activities. These systems can for example occur in industrial production. In this paper, we apply VSS to reconstructing workflows in a job shop. The example application was designed in cooperation with logistics experts to resemble a real job shop. In order to deal with probabilistic decisions in the workflows, we extended Hidden non-Markovian Models to include immediate transitions and modified the corresponding Proxel-based behavior reconstruction algorithm accordingly. Experimental data was acquired using a setup including a printed layout, RFID sensors and a central data collection unit. We were able to reconstruct the actual workflows from the acquired sensor data, which for the first time shows an application of VSS to real measurement data. This first practical application, and the extension of the modeling paradigm takes us forward to our goal of a realistically applicable method for behavior reconstruction based on partial system observations.