Representing human-automation challenges
Automation technology is widely implemented in process control domains due to its benefits of improving efficiency and enhancing control. However, use of automation also introduces an often complex intermediary between the human and the controlled domain, which can obscure from the operator how system functioning is achieved. The difficulty for operators to perceive and understand what the automatic system is doing has a potentially negative impact on overall system performance, since the human operator perform important functions in the work system related to both safety and production.
In this thesis it is argued that there are few approaches that address the problem of specifically, and each existing approach might individually not cover the entire problem scope in full detail. Further, current methodologies seem to have difficulties in reaching applications apart from narrow human factors engineering practices.
With this background in mind, the research work presented in this thesis has focused on how human-automation related challenges can be addressed to improve preconditions for operators in understanding automatic system functioning. Creating the appropriate preconditions in control environments is a multidisciplinary design challenge striving for safe and efficient work systems. The purpose of this thesis was to aid human factors engineering practitioners in industry in dealing with this challenge.
To fulfil the purpose, an existing theoretical model was adapted and used to describe human-automation challenges in general. This led to a theoretical unification of human-automation related challenges and a way to describe challenges systematically. The unified format enables description and analysis of automated human-machine systems in order to identify representational gaps and matches in the work system. The theoretical model was then used as a basis for developing a method named the “System Representation Matrix”. The System Representation Matrix enables description and analysis of the dynamic domain, the control system, the control system user interface and the necessary operator knowledge, in a unified representation.
Conclusions from testing and evaluating the method are that the System Representation Matrix can aid creating an overview of automated human-machine systems. The overview has potential as an aid for reasoning about matters of system functioning and design. In practice, the matrix could provide support for design decisions, help define necessary operator knowledge and become a tool to aid human factors engineering in multidisciplinary teams. This has the potential to lead to improved aid for human factors engineers when dealing with human-automation challenges in industrial practice.
cognitive systems engineering