One of the foundations for Industry 4.0 is the integration of various industrial elements (i.e. sensors, machines, and services) so that these devices can decide in a relatively autonomous way the level of integration which will be adopted. Thus, it is important to understand how the communication Machine to Machine is effectively realized and how these data can be explored and used to enhance the manufacturing process. The exchange of information between machines in the industrial process represents a potential to acquire and analyze a mass of data characterized as "big data", which can be perceived as an opportunity to discuss the paradigms of the industrial systems. Therefore, the purpose of this research is to identify the requirements for the Machine to Machine communication and the use of this data/information for more complexes analyzes using big data and analytics techniques. The KAOS methodology was utilized to model these requirements.
In an Industry 4.0 (I4.0) context there is significant increase in information exchange and storage through the interaction among assets (machines, systems, and people). These data are important because it can lead to the autonomy of assets in decision making. However, the entire organization of I4.0 assets in terms of the quantity and the quality of information to be managed makes the system very complex. Thus, a systematic is needed to deal with this complexity where reference architectures can be used to identify the functionality required to handle this large amount and diversity of data, and how they can be organized. Therefore, the aim here is the specification of a big data acquisition process for its implementation within I4.0 context to ensure quality data for analysis and decision making. The proposed solution is based on reference architectures NBDRA and RAMI 4.0.
In an Industry 4.0 (I4.0) context there is significant increase in information exchange and storage through the interaction among assets (machines, systems, and people). These data are important because it can lead to the autonomy of assets in decision making. However, the entire organization of I4.0 assets in terms of the quantity and the quality of information to be managed makes the system very complex. Thus, a systematic is needed to deal with this complexity where reference architectures can be used to identify the functionality required to handle this large amount and diversity of data, and how they can be organized. Therefore, the aim here is the specification of a big data acquisition process for its implementation within I4.0 context to ensure quality data for analysis and decision making. The proposed solution is based on reference architectures NBDRA and RAMI 4.0.
The development of new technologies and ways of acquiring and processing data obtained at all stages of an industrial process is perceived by experts as one strategic pillar of a new industrial revolution, called "Industry 4.0". Industry 4.0 can be understood as the result of the implementation, in fact, of intelligent factories where production systems are autonomous, versatile and associated with services to meet the different needs of each consumer. This new industrial revolution relies on concepts such as the Internet of Things (IoT), where all the devices involved in productive systems are connected together, composing cyber-physical systems (CPS) so that a very large amount of data directly and indirectly related to this system can be processed both in the virtual world, and in the real world.In parallel with the development of these productive systems, new methods of handling a very large set of data known as big data are being considered. These studies explore multidisciplinary techniques and approaches, that is, enhancing the ability to acquire and potentially analyze any data related to processes and products is interpreted as an opportunity to review the paradigms associated with production systems.The large amount and variety of data in the productive system, in the context of big data, is evidently a fundamental source of information to control and optimize all stages of the production process and associated services. In this context, this work has as objectives to develop (i) the modeling of the data acquisition process of a productive system and, (ii) a data acquisition system architecture proposal.Based on the nature of the production system, the system approach to discrete events is adopted for the representation / description of the processes involved, using the PFS (Production Flow Schema) / Petri Net technique. The architecture of the big data acquisition system is proposed taking into consideration the concepts present in RAMI 4.0 which is a reference architecture for Industry 4.0.
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