Safety-Critical Components Analysis Using Knowledge Graph For CNC Machine

A CNC machine may contain various types of faults and may harm production safety in multiple ways. It is difficult for managers to determine which faults are more severe due to the complex structure of the machine. This further confuses managers to generate efficient preventions and responses. Consequently, there is a need to develop a method to assist production managers to identify the safety-critical components (SCCs) in a CNC machine. The current work developed a solid framework for SCCs examination using a riskbased knowledge graph method. Firstly, we summarize the structure of the CNC machine using ontology and examine CNC faults and corresponding consequences. The reasoning between faults and consequences is via literature examination using a web-crawler. This is followed by using BiLSTM-CRF for knowledge processing and using TransE for entity alignment. Then, a risk-based knowledge graph is developed via developing a weight scale for the severity of consequence; and using historical fault data to access frequency. The knowledge graph visualization is developed using Gephi, and SCCs can be virtually determined with deeper color in nodes as the darker color represents higher risk. This has the potential to help industry practitioners better understand their current operations situation and generate responses to avoid fault occurrence and improve production safety.