Automatic generation of integrated process data visualizations using human knowledge

The increasing complexity of industrial processes leads to complex process visualizations. Amongst other things, this is often due to the fact that a visualization engineer does not have deep knowledge of all physical and logical relations inside a plant. Additionally, different operators have to work with the same visualization despite the fact, that their personal preferences, abilities and needs differ. Having to work with unclear and confusing visualizations leads to an increased workload for plant operators and thus to higher error rates. Due to cost and time constraints, creating better or user-specific visualizations manually is not possible, especially because taking the operators’ specific knowledge and experience into account is difficult. This paper presents a concept to automatically generate process visualizations and support systems by the usage of a knowledge base and an influence model. This allows for operator-specific visualizations, considering preferences, abilities and needs. It also eases the visualization engineer’s work by automatically choosing suitable diagrams and their properties. Additionally, by providing a system to acquire the operator’s knowledge, complex relations inside a plant can be made accessible and utilized for optimizing the production process and visualization.