Madsen, Anders L. Sondberg-Jeppesen, Nicolaj Sayed, Mohamed S. Peschl, Michael Lohse, Niels Applying Object-Oriented Bayesian Networks for smart diagnosis and health monitoring at both component and factory level To support health monitoring and life-long capability management for self-sustaining manufacturing systems, next generation machine components are expected to embed sensory capabilities combined with advanced ICT. The combination of sensory capabilities and the use of Object-Oriented Bayesian Networks (OOBNs) supports self-diagnosis at the component level enabling them to become self-aware and support self-healing production systems. This paper describes the use of a modular component-based modelling approach enabled by the use of OOBNs for health monitoring and root-cause analysis of manufacturing systems using a welding controller produced by Harms & Wende (HWH) as an example. The model is integrated into the control software of the welding controller and deployed as a SelComp using the SelSus Architecture for diagnosis and predictive maintenance. The SelComp provides diagnosis and condition monitoring capabilities at the component level while the SelSus Architecture provides these capabilities at a wider system level. The results show significant potential of the solution developed. Object Oriented Bayesian Networks;Software architecture;Real-world application;Mechanical Engineering not elsewhere classified 2017-03-22
    https://repository.lboro.ac.uk/articles/conference_contribution/Applying_Object-Oriented_Bayesian_Networks_for_smart_diagnosis_and_health_monitoring_at_both_component_and_factory_level/9554948