Node-Red and OPC UA Based Lightweight and Low-Cost Historian with Application in the Water Industry

Nicolae, Andrei; Korodi, Adrian

doi:10.1109/indin.2018.8471928

Cited by 11 publications

(15 citation statements)

References 17 publications

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“…Besides the WTP SCADA, the OPC UA may be present at the PLC level directly or using OPC UA based wrappers/gateways as developments in [43,44]. Following data gathering and dependency analysis from [19] and [45], quality indicators are identified for the WWs. These quality indicators are considered in the current paper in relation to the total energy consumption used for treating and distributing the water.…”

Section: Increasing Energy Efficiencymentioning

confidence: 99%

“…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.The classical automation pyramid is transforming under the Industry 4.0 guidance into a different structure that facilitates direct connection and communication between entities [6-8].The current functional solutions from the industrial environment consist of a large variety of dispersed systems, both chronologically and by location, leading to the necessity of ensuring the interoperability of those solutions.Although the heterogeneity of devices used in industry naturally supported the appearance of many communication protocols (see [9][10][11]), the Open Platform Communications Unified Architecture (OPC UA) was gradually imposed as the standard IIoT protocol.The studies from [12][13][14] analyze the current OPC UA implementations regarding Industry 4.0 specific digitalization solutions, while [15][16][17][18] have successfully used OPC UA for increasing both interoperability and connectivity of different automation systems, thus emphasizing that OPC UA is an essential requirement for any Industry 4.0 compliant technical system.The superior connectivity and interoperability between entities from technical systems under the Industry 4.0 guidance have led to the appearance of the data accumulation concept, which is primarily implemented in industry using historian applications [19,20].The data gathered by historian applications is mostly unused, although it opens new possibilities in the proactive historian applications area. Research towards stored data analysis algorithms and optimization strategies must provide insights into pattern identification, learning capabilities, and solid conclusions regarding real-world optimizations.…”

mentioning

confidence: 99%

“…The superior connectivity and interoperability between entities from technical systems under the Industry 4.0 guidance have led to the appearance of the data accumulation concept, which is primarily implemented in industry using historian applications [19,20].…”

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confidence: 99%

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Industrial Internet of Things and Fog Computing to Reduce Energy Consumption in Drinking Water Facilities

et al. 2020

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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.

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Section: Increasing Energy Efficiencymentioning

confidence: 99%

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confidence: 99%

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Industrial Internet of Things and Fog Computing to Reduce Energy Consumption in Drinking Water Facilities

et al. 2020

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“…A more general approach is presented in [28], where the authors proposed a low-cost and lightweight Historian based on OPC UA, which made it potentially available for a wide range of industries. The solution embedded Node-RED into a Java application and stored the collected data inside an SQLite database.…”

Section: Interoperability and Historianmentioning

confidence: 99%

“…The proposed architecture facilitates creation of an autonomous, proactive Historian able to optimize and influence a functional system without human assistance. Extending the research from [28], where a basic low-cost Historian was developed, the current research presents a stored data analysis algorithm that identifies data dependencies between measured characteristic and reference characteristic, establishes degrees of dependency, and exposes functional patterns. The proposed solution is aware of the process, so that computing or process-related degrees of freedom are considered (e.g., parameter and functional limitations, output possibilities).…”

Section: Towards a Proactive Historian Application In The Water Industrymentioning

confidence: 99%

Identifying Data Dependencies as First Step to Obtain a Proactive Historian: Test Scenario in the Water Industry 4.0

Nicolae

Korodi

Silea

2019

Water

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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.

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Comparing Two Different Implementations of OPC UA Clients

Covrig,

Ciobotaru,

Duka

et al. 2024

Lecture Notes in Networks and Systems

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Node-Red and OPC UA Based Lightweight and Low-Cost Historian with Application in the Water Industry

Cited by 11 publications

References 17 publications

Industrial Internet of Things and Fog Computing to Reduce Energy Consumption in Drinking Water Facilities

Industrial Internet of Things and Fog Computing to Reduce Energy Consumption in Drinking Water Facilities

Identifying Data Dependencies as First Step to Obtain a Proactive Historian: Test Scenario in the Water Industry 4.0

Comparing Two Different Implementations of OPC UA Clients

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