Semantic Diagnostics of Smart Factories.

Smart factories are one of the biggest trends in modern manufacturing, also known as Industry 4.0. They reach a new level of process automation and make heavy use of sensors in manufactoring equipment, which brings new challenges to monitoring and diagnostics at smart factories. We propose to address the challenges with a novel rule-based monitoring and diagnostics language that relies on ontologies and reasoning and allows one to write diagnostic tasks at a high level of abstraction. We show that our approach speeds up the diagnostic routine of engineers at Siemens: they can formulate and deploy diagnostic tasks in factories faster than with existing Siemens data-driven solutions. Moreover we show that our diagnostic language, despite the built-in reasoning, allows for efficient execution of diagnostic tasks over large volumes of industrial data. Finally, we implemented our ideas in a prototypical diagnostic system for smart factories.