Predictive situation awareness model for smart manufacturing

Smart manufacturing relies on a combination of different sources providing key information to support diverse activities throughout the manufacturing process. Most smart manufacturing systems focus on activities directly related to the management of robots, conveyor belts, maintenance logs, and others that ensure the process runs smoothly. An initial step to support such smart manufacturing systems is an awareness process for estimating current situations and predicting future situations in manufacturing, called Predictive Manufacturing Situation Awareness (MSAW). Our research addresses developing an MSAW system with the goal of enhancing industrial competitiveness (e.g., lower cost in shorter time with higher quality) for the manufacturing industry. This requires constant monitoring of market conditions, prices of manufacturing assets, and other inputs that would help to define how the production line behaves. This input is highly stochastic, which makes fusing the data from the diverse sources a challenge. In such situations, the MSAW system requires efficient knowledge representation for various situations and expeditious reasoning methods for estimating current situations as well as predicting future situations. In this paper, we provide an overview of the data fusion process supporting MSAW, including the representation of situations with associated uncertainty, and reasoning methods to support improved manufacturing processes.