Current efforts towards achieving better connectivity and increasing intelligence in functioning of industrial processes are guided by the Industrial Internet of Things paradigm and implicitly stimulate occurrence of data accumulation. In recent years, several researchers and industrial products have presented Historian application solutions for data accumulation. The large amounts of data that are gathered by these Historians remains mostly unused or used only for reporting purposes. So far, Historians have been focused on connectivity, data manipulation possibilities, and sometimes on low-cost solutions in order to gain higher applicability or to integrate multiple SCADA servers (e.g. Siemens–WinCC, Schneider Electric – Vijeo Citect, IGSS, Wonderware, InduSoft Web Studio, Inductive Automation – Ignition, etc.), etc. Both literature and industry are currently unable to identify a Historian solution that functions in fog and efficiently applies and is built upon Industry 4.0 ideas. The future is to conceive a proactive Historian that is able to, besides gathering data, identify dependencies and patterns for particular processes and elaborate strategies to increase performance in order to provide feedback through corrective action on the functional system. Using available solutions, determining patterns by the Historian operator in the context of big data is a tremendous effort. The motivation of this research is provided by the currently unoptimized and partly inefficient systems in the water industry that can benefit from cost reduction and quality indicator improvements through IIoT concepts related to data processing and process adjustments. As the first part of more complex research to obtain a proactive Historian, the current paper wishes to propose a reference architecture and to address the issue of data dependency analyses as part of pattern identification structures. The conceptual approach targets a highly customizable solution considering the variety of industrial processes, but it also underlines basic software modules as generally applicable for the same reason. To prove the efficiency of the obtained solution in the context of real industrial processes, and their corresponding monitoring and control solutions, the paper presents a test scenario in the water industry.
The industry is generally preoccupied with the evolution towards Industry 4.0 principles and the associated advantages as cost reduction, respectively safety, availability, and productivity increase. So far, it is not completely clear how to reach these advantages and what their exact representation or impact is. It is necessary for industrial systems, even legacy ones, to assure interoperability in the context of chronologically dispersed and currently functional solutions, respectively; the Open Platform Communications Unified Architecture (OPC UA) protocol is an essential requirement. Then, following data accumulation, the resulting process-aware strategies have to present learning capabilities, pattern identification, and conclusions to increase efficiency or safety. Finally, model-based analysis and decision and control procedures applied in a non-invasive manner over functioning systems close the optimizing loop. Drinking water facilities, as generally the entire water sector, are confronted with several issues in their functioning, with a high variety of implemented technologies. The solution to these problems is expected to create a more extensive connection between the physical and the digital worlds. Following previous research focused on data accumulation and data dependency analysis, the current paper aims to provide the next step in obtaining a proactive historian application and proposes a non-invasive decision and control solution in the context of the Industrial Internet of Things, meant to reduce energy consumption in a water treatment and distribution process. The solution is conceived for the fog computing concept to be close to local automation, and it is automatically adaptable to changes in the process’s main characteristics caused by various factors. The developments were applied to a water facility model realized for this purpose and on a real system. The results prove the efficiency of the concept.
With the recent advances in the area of OPC UA interfacing and the continuously growing requirements of the industrial automation world, combined with the more and more complex configurations of ECUs inside vehicles and services associated to car to infrastructure and even car to car communications, the gap between the two domains must be analyzed and filled. This gap occurred mainly because of the rigidness and lack of transparency of the software-hardware part of the automotive sector and the new demands for car to infrastructure communications. The issues are related to protocols as well as to conceptual views regarding requirements and already adopted individual directions. The industrial world is in the Industry 4.0 era, and in the Industrial Internet of Things context, its key interfacing enabler is OPC UA. Mainly to accommodate requirements related, among others, to high volumes, transfer rates, larger numbers of nodes, improved coordination and services, OPC UA enhances within its specifications the Publish-Subscribe mechanism and the TSN technology. In the OPC UA context, together with the VSOME/IP Notify-Subscribe mechanism, the current work is stepping toward a better understanding of the current relation between the needs of the industry and the suitable technologies, providing in-depth analysis on the most recent paradigms developed for data transmission, taking in consideration the real-time capabilities and use-cases of high concern in automation and automotive domains, and toward obtaining a VSOME/IP—OPC UA Gateway that includes the necessary characteristics and services in order to fill the protocol-related gap between the above mentioned fields. The developed case study results are proving the efficiency of the concept and are providing a better understanding regarding the impact between ongoing solutions and future requirements.
Communication protocols are evolving continuously as the interfacing and interoperability requirements are the foundation of Industry 4.0 and Industrial Internet of Things (IIoT), and the Open Platform Communication Unified Architecture (OPC UA) protocol is a major enabling technology. OPC UA was adopted by the industry, and research is continuously carried out to extend and to improve its capabilities, to fulfil the growing requirements of specific industries and hierarchical levels. Consistent issues that have to be approached are related to the latest specifications and the real-time context that could extend the applicability of the protocol and bring significant benefits in terms of speed, data volumes, footprint, security. The real-time context is essential in the automotive sector and it is highly developed within some specific protocols. The current work approaches first the conceptual analysis to improve the OPC UA interfacing using the Publish-Subscribe mechanism, focusing on real-time constraints and role distribution between entities, and considering some well-founded interfacing strategies from the automotive sector. The conceptual analysis is materialized into a solution that takes OPC UA Publish-Subscribe over User Datagram Protocol (UDP) mechanism to the next level by developing a synchronization algorithm and a multithreading broker application to obtain real time responsiveness and increased efficiency by lowering the publisher and the subscriber footprint and computational effort, reducing the difficulty of sending larger volumes of data for various subscribers and the charge on the network and services in terms of polling and filtering. The proof of concept is evaluated and the results prove the efficiency of the approach and the solution.
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