Faculty of Computer Science

Research Group Theoretical Computer Science

Oberseminar: Heterogene formale Methoden

Abstract:

Industrial risk assessment follows a series of logical steps to identify and examine any potential hazards associated with a particular machinery and its sub-components. The ENISO 12100 lists the basic hazard analysis and risk evaluation procedures. It also provides a guideline on the safety of a machinery. Such risk assessment is important to make a decision on the safety of machinery for risks to be reduced where necessary. For a Testing, Inspection and Certification (TIC) industry, within the scope of certification, such kind of risk assessment must be checked and commented on them if necessary. This is done manually, which is expensive and time consuming. Also, the assessments performed in the present day scenario are based on the requirements listed in safety standards. These requirements are sometimes fuzzy and difficult to interpret. The goal of this project is towards finding an approach to make faster, safer and more efficient risk assessments. The approach used is towards developing a semantic knowledge model from safety standards and human experts. With the connected infrastructure of industry 4.0 and smart machines, such kind of machine-readable representation of knowledge can help for faster risk assessments. They can provide a more refined interpretation of requirements for risk assessments and also preserve knowledge for quicker re-assessments. The project deals with the safety assessment of a laser cutting machine using a knowledge graph based on ontology.