Semantically-enhanced rule-based diagnostics for industrial Internet of Things: The SDRL language and case study for Siemens trains and turbines

An Industrial Internet of Things (IoT) is a network of intelligent industrial equipment such as trains and power generating turbines that collect and share large amounts of data. These data are either generated by various sensors deployed in the equipment or captures equipment specific information such as configurations, history of use, and manufacturer. Diagnostics of the industrial IoT is critical to minimise the maintenance cost and downtime of its equipment. It is common that industry today employs rule-based diagnostic systems for this purpose. Rules are typically used to process signals from sensors installed in equipment by filtering, aggregating, and combining sequences of time-stamped measurements recorded by the sensors. Such rules are often data-dependent in the sense that they rely on specific characteristics of individual sensors and equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers especially when the rules are applied in industrial IoT scenarios. In this work we propose an approach to address these problems by relying on the well-known Ontology-Based Data Access approach: we propose to use ontologies to mediate the sensor signals and the rules. To this end, we propose a semantic rule language, SDRL, where signals are first class citizens. Our language offers a balance of expressive power, usability, and efficiency: it captures most of Siemens data-driven diagnostic rules, significantly simplifies authoring of diagnostic tasks, and allows to efficiently rewrite semantic rules from ontologies to data and execute over data. We implemented our approach in a semantic diagnostic system and evaluated it. For evaluation, we developed a use case of rail systems as well as power generating turbines at Siemens and conducted experiments to demonstrate both usability and efficiency of our solution.