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Big Data Semantics in Industry 4.0 Big Data Semantics in Industry 4.0 Marek Obitko1() and Václav Jirkovský1,2 1 Rockwell Automation Research and Development Center, Pekařská 695/10a, Prague, Czech Republic {mobitko,vjirkovsky}@ra.rockwell.com 2 Czech Technical University in Prague, Zikova 4, Prague, Czech Republic Abstract. The Industry 4.0 is a vision that includes connecting more intensively physical systems with their virtual counterparts in computers. This computeri- zation of manufacturing will bring many advantages, including allowing data gathering, integration and analysis in the scale not seen earlier. In this paper we describe our Semantic Big Data Historian that is intended to handle large vo- lumes of heterogeneous data gathered from distributed data sources. We de- scribe the approach and implementation with a special focus on using Semantic Web technologies for integrating the data. Keywords: Industry 4.0 · Cyber-Physical Systems · Big Data · Semantics · Internet of Things · Industrial automation · Heterogeneity 1 Introduction One of the issues that the automation industry has to handle more and more is the amount of data that needs to be processed and analyzed for rapid decision making for improved productivity. The need for smart analytics tools is emphasized by so called “Industry 4.0”, which is a transformation towards the fourth industrial revolution. It is an effort led by Germany with the vision to connect physical systems, such as manu- facturing plants, with virtual models in computers, which will allow and provide au- tomation in the scale not seen earlier. This movement can be in short described as the “computerization of manufacturing.” In this paper we discuss our contribution that we call Semantic Big Data Historian which is intended to handle large volumes of heterogeneous data gathered from dis- tributed data sources, such as sensors. Following the “computerization of manufactur- ing” we use the technologies for handling Big Data and semantics used by internet companies and apply them to physical systems such as manufacturing plants. This paper is organized as follows: first, we discuss the components of Industry 4.0, especially the Cyber-Physical Systems. Then we provide a description of Big Data features and also discuss Semantic Web effort together with a related ontology for sensor data. The main contribution of the paper is the following description of Big Data Historian – its motivations and goals, description and gathering of data, includ- ing a sample case study. We conclude the paper with conclusions and outlook. © Springer International Publishing Switzerland 2015 V. Mařík et al. (Eds.): HoloMAS 2015, LNAI 9266, pp. 217–229, 2015. DOI: 10.1007/978-3-319-22867-9_19 218 M. Obitko and V. Jirkovský 2 Industry 4.0 and Cyber-Physical Systems The Industry 4.0 is the term used for fourth industrial revolution, following the three previous industrial revolutions. One of the differences from the previous revolutions is that this movement is predicted and in fact pushed a-priory – in other words, it is not observed ex-post. The economic impacts are predicted to be huge due to opera- tional effectiveness, new business models, services and products. Some technologies, such as Internet of Things (IoT), Internet of Services (IoS) were introduced earlier than the concept of Industry 4.0, but are critical for Industry 4.0. On the other hand, Cyber-Physical Systems (CPS) were introduced as a paradigm within this context. Clear and exact definition of Industry 4.0 is not provided, but it is usually characte- rized by its vision and description of basic technologies and selected scenarios [6]. The main design principles are as follows: • Interoperability – companies, CPS and humans are connected over IoT and IoS. Standardization and semantic descriptions are important. • Virtualization – CPS are able to monitor physical processes. In other words, the data from sensors are linked to virtual and simulation models. • Decentralization – it is difficult to control inherently distributed systems centrally, so individual agents have to make decisions on their own and propagate only fail- ures or complex decisions to higher levels. • Real-time capability – the constant data analysis is needed to immediately react to any changes in the environment, such as routing or handling failures. • Service orientation – service oriented architecture (SOA) allows encapsulation of various services to combine them together and to facilitate their utilization. • Modularity – it is needed to be able to easily adjust or add modules and to utilize new modules immediately. According to literature review in [6], the Industry 4.0 can be also characterized by its main components. Let us briefly review the components. 2.1 Cyber-Physical systems Cyber-Physical systems (CPS) are “integrations of computation and physical processes. Embedded computers and networks monitor and control the physical processes, usually with feedback loops where physical processes affect computations and vice versa” [9]. The CPS provide the fusion of physical and virtual world, i.e., integration of computation and physical processes. This can be achieved by unique identification (for example RFID tags), both centralized and decentralized storage and analytics, with many sensors and actuators communicating over a network. An example is a virtual battery [10] – a battery in electric car has its virtual coun- terpart updated in real time, which allows diagnostics, simulation, prediction etc. for better customer experience. The battery pack is one of the most significant compo- nents of an electric vehicle and the uncertainty in the driving range, the reliability and life of batteries, including safety concerns, are all challenges that must be overcome. Big Data Semantics in Industry 4.0 219 Therefore monitoring and processing of various factors of batteries is needed to pro- vide for example failure prediction and for the possibility to improve the health of batteries. The individual monitoring of individual batteries separately is only the first step in usefulness – connecting the physical system to a virtual system where the data from many data sources can be combined together allows solutions such as prognos- tics, visualization, simulation and predictions at a higher level with combined data. This can help individual vehicles to improve the usage of batteries, but also provides help to battery manufacturers. It is expected that longer term data acquisition and analytics would allow further detailed study and improvements of batteries in electric vehicles in general. Another example discussed in [10] is predictive health monitoring solution for industrial robots in an assembly line. Again, long term acquisition of data and con- necting them in virtual world and models allows for monitoring, simulation and predictions. 2.2 Internet of Things The Internet of Things (IoT) [1, 4] is a network of physical systems that are uniquely identified and can interact to reach common goals. The “things” in IoT are sensors, actuators, communication modules, devices that can cooperate together with neigh- boring smart components to reach goals that could not be achieved without this coop- eration. In other words, the IoT can be described as a network where CPS cooperate with each other through unique addressing schemas. An example is a Smart Home where connected devices such as various environ- mental sensors, components of heating and ventilation system and for example mobile phones are connected to provide more comfort. The visions of smart homes are cur- rently expanded from the user-friendly automation of environmental conditions such as temperature and humidity to connecting even more devices together, such as kitch- en equipment devices. Similar examples are Smart Grids and within the context of Industry 4.0 especially Smart Factories which are discussed in detail later. 2.3 Internet of Services The Internet of Services (IoS) is the concept of offering the services over Internet so that they can be combined into a value-added services by various suppliers. The IoS consists of the service vendors providing the services themselves, infrastructure for services and business models. The formed services are then accessed by consumers. The applicability of this approach ranges from single manufacturing plants to net- works of factories to allow special production technologies. An example is forming virtual production technologies and capabilities by combin- ing individual services as needed to carry out a complex task while combining differ- ent skills and observing time or financial restrictions. 220 M. Obitko and V. Jirkovský 2.4 Smart Factory Smart factories are often mentioned as a key feature of Industry 4.0. In fact they are the realization of the other mentioned components in the context of a factory. The basic principle is that information coming from physical and virtual world is used to provide context and assistance for people and machines to execute their tasks. By context information such as position or status is meant – the information from envi- ronment is important as well, and was not usually considered much in the earlier ap- proaches. The work of a Smart Factory is driven by customer demands and utilizes CPS, IoT and IoS to satisfy the orders. An example of Smart Factory component is intelligent work piece carrier. These carriers are able to route the work pieces based on both required destination(s) and conditions in the transport system [15]. 2.5 Other Components Other Industry 4.0 components that we will not discuss in detail here include: • Smart Product – the realization of CPS in a product. The Smart Product is situated, personalized, adaptive, pro-active and aware of business and location constraints. A Smart Product is smart from the time of its manufacturing, through the time of its time use by a consumer, till the end of its life. Additional services are provided in addition to a physical product. • Machine to Machine (M2M) – communication between devices of the same or similar type. The communication between machines thorough IoT spans from one-to-one connections to a system of networks.
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