Modeling Operator Involvement in Automated Production: A Case Study of the Functional Resonance Analysis Method

Faults in the technical process of automated production systems (aPS) are challenging to detect, report, and recover accurately. Missing physical equipment, lacking detection mechanisms in the control software, and insufficient training and experience of human operators lead to an often delayed and imprecise detection of a fault's root cause. Since fault detection and recovery typically rely heavily on human involvement, there is also a risk of operators performing only partial repairs or inadvertently introducing new faults. Thereby, even slight deviations in human-machine interactions may affect process outcomes unpredictably, which so far cannot be modelled in field-level automation. The Functional Resonance Analysis Method (FRAM) is well-suited to address functional relations in such complex socio-technical systems and accurately represent resource requirements, preconditions, control, and time constraints, making it a promising tool to methodically investigate aPS faults. Yet, the FRAM has barely been applied in this domain. This paper thus introduces an approach relying on the FRAM to methodically derive alarm conditions and system extensions as a foundation for broader uses to investigate in future work. The FRAM-based approach was shown to reduce uncaught faults, required operator interventions, and thus unplanned downtime, in an experiment with two participants on a lab-sized demonstrator machine.