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A Bayesian predictive assistance system for resource optimization - a case study in industrial cleaning process

: Shresta, Ganesh Man; Niggemann, Oliver


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-:
ETFA 2014, 19th IEEE International Conference on Emerging Technologies and Factory Automation : 16.- 19 September 2014, Barcelona, Spain
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-4846-8
ISBN: 978-1-4799-4845-1
ISBN: 978-1-4799-4844-4
6 S.
International Conference on Emerging Technologies and Factory Automation (ETFA) <19, 2014, Barcelona>
Fraunhofer IOSB ()

Optimizing the resource consumption by the products (machines) and making them environment friendly is the aim of almost all producers today. May it be due to cost of resources, their limited availability, their affect on the environment or consumer awareness. Ample research is being carried out at national and international level for resource optimization. Adding intelligence and learning capability is being increasingly used as an approach for resource optimization. Different methods and models for machine learning are available in the literature. Bayesian network is one of the widely used learning model for resource optimization in wide range of applications [1], [2]. In this paper, we present the use of Bayesian network for resource optimization and decision support system in an industrial cleaning process. The proposed Bayesian predictive assistance system assists the cleaner in choosing the optimal parameters and would be a self-learning system that stores the successful cleaning results in a global database for future cleaning cycle.