A framework for automatic knowledge-based fault detection in industrial conveyor systems

In this paper, a framework for automatic generation of a flexible and modular system for fault detection and diagnosis (FDD) is proposed. The method is based on an ontology-based integration framework, which gathers the information from various engineering artifacts. Based on the ontologies, FDD functions are generated based on structural and procedural generation rules. The rules are encoded as SPARQL queries which automatically build logical segments of the entire manufacturing system in the ontology, assign sub-processes to these segments, and finally generate the appropriate FDD system for the sub-process. These generated modular FDD functions are additionally combined in a modular way to enable the fault detection and diagnosis of the entire system. The effectiveness of the approach is demonstrated by a first application to a conveyor system.