Author(s): Evgeny Kharlamov, Ognjen Savkovic, Martin Ringsquandl, Guohui Xiao, Gulnar Mehdi, Elem Guzel Kalayci, Werner Nutt, Mikhail Roshchin, Ian Horrocks, Thomas Runkler
Abstract: Automation is one of the biggest trends in modern manufacturing known as Industry 4.0. Automation brings new challenges to diagnostics of equipment and monitoring of processes in factories. We propose to address the challenges with a novel rule-based monitoring and diagnostics language that relies on ontologies and reasoning and allows to write diagnostic tasks in a high level of abstraction. We show that our approach speeds up the diagnostic routine of engineers in Siemens: they can formulate and deploy diagnostic tasks in factories faster than with existing Siemens data-driven diagnostic languages. Moreover, via analyses of the computational complexity and experimentally, 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 prototypal diagnostic system for automated factories.
Keywords: Ontologies; Rule Based Diagnostics; Automated manufactoring