Model-Driven Diagnostics Generation for Industrial Automation

We propose a methodology for overcoming the current approach of writing diagnostics code for industrial automation applications after the system is designed, which results in significant extra effort/cost, and potential discrepancies between design and diagnostics output. We show how we can automatically generate diagnostics from a more complex simulation model. We show how a model-transformation framework can transform a hybrid-systems simulation model into a propositional-logic diagnostics model with appropriate transformation rules. We illustrate our approach with an example from the domain of control for building lighting systems.