Machine Learning-Supported Designing of Human–Machine Interfaces

Abstract

The design and functionality of the human–machine interface (HMI) significantly affects operational efficiency and safety related to process control. Alarm management techniques consider the cognitive model of operators, but mainly only from a signal perception point of view. To develop a human-centric alarm management system, the construction of an easy-to-use and supportive HMI is essential. This work suggests a development method that uses machine learning (ML) tools. The key idea is that more supportive higher-level HMI displays can be developed by analysing operator-related events in the process log file. The obtained process model contains relevant data on the relationship of the process events, enabling a network-like visualisation. Attributes of the network allow us to solve the minimisation problem of the ideal workflow–display relation. The suggested approach allows a targeted process pattern exploration to design higher-level HMI displays with respect to content and hierarchy. The method was applied in a real-life hydrofluoric acid alkylation plant, where a proposal was made about the content of an overview display.