Supporting commissioning of production plants by model-based testing and model learning

During the commissioning phase of production systems the identification and correction of malfunctions is a tedious task mainly done manually by commissioning engineers. This task is of high importance because missed malfunctions may result in hazardous behavior during operation phase. At this point, regardless of the engineers expertise a systematic support can drastically decrease the risk of missed malfunctions. A promising systematic approach is to use engineering artifacts of the system design phase as an information source to identify unexpected behavior regarding the specification. This paper proposes such a systematic approach based on model-based testing resulting in automatic test case generation and execution which allows to support engineers with learned models representing the expected transient system behavior. Subsequently, the obtained models are used for detection of unexpected behavior during commissioning. The unexpected behavior is presented to a commissioning engineer who decides if the behavior (1) is correct and will be added to the models or (2) represents an identified system malfunction. The approach is evaluated on a demonstration plant.