Applying Object-Oriented Bayesian Networks for Smart Diagnosis and Health Monitoring at both Component and Factory Level

Abstract

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.

Parameter Learning Algorithms for Continuous Model Improvement Using Operational Data

“…These components have the ability to collect data from sensors that are mounted physically in the same device or in a near location and fuse this data to extend its models capabilities. The Linear Axis OOBN has been encapsulated in the Machine SelComp for the Linear Axis [20,12], see Figure 3, as it represents a field device, machine or its subcomponents. The goal is to provide diagnostic capabilities at component-level supporting system-level diagnostics.…”