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.

工业物联网（IoT）是智能工业设备的网络，例如火车和发电涡轮机，它们收集并共享大量数据。这些数据要么由设备中部署的各种传感器生成，要么捕获设备特定的信息，例如配置，使用历史和制造商。工业物联网的诊断对于最大程度地降低其设备的维护成本和停机时间至关重要。当今行业通常为此目的采用基于规则的诊断系统。规则通常用于处理来自安装在设备中的传感器的信号，方法是过滤，汇总和组合由传感器记录的带时间戳的测量序列。这些规则通常依赖于数据，因为它们依赖于各个传感器和设备的特定特征。这种依赖性给工程师在规则编写，重用和维护方面提出了严峻的挑战，尤其是在将规则应用于工业物联网场景中时。在这项工作中，我们提出了一种基于众所周知的基于本体的数据访问方法来解决这些问题的方法：我们建议使用本体来调解传感器信号和规则。为此，我们提出了一种语义规则语言SDRL，其中信号是一等公民。我们的语言在表达能力，可用性和效率之间取得了平衡：它捕获了大多数Siemens数据驱动的诊断规则，显着简化了诊断任务的编写，并允许有效地将语义规则从本体重写为数据并在数据上执行。我们在语义诊断系统中实现了我们的方法并对其进行了评估。为了进行评估，我们在西门子开发了一个铁路系统以及发电涡轮机的用例，并进行了实验以证明我们解决方案的可用性和效率。