Recent advances on industrial data-driven energy savings: Digital twins and infrastructures

System identification has been a major advancement in the evolution of engineering. As it is by default the first step towards a significant set of adaptive control techniques, it is imperative for engineers to apply it in order to practice control. Given that system identification could be useful in creating a digital twin, this work focuses on the initial stage of the procedure by discussing simplistic system order identification. Through specific numerical examples, this study constitutes an investigation on the most “natural” method for estimating the order from responses in a convenient and seamless way in time-domain. The method itself, originally proposed by Ho and Kalman and utilizing linear algebra, is an intuitive tool retrieving information out of the data themselves. Finally, with the help of the limitations of the methods, the potential future outlook is discussed, under the prism of forming a digital twin.

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Reducing the Energy Budget in WSN Using Time Series Models

Wireless Communications and Mobile Computing ◽

10.1155/2020/8893064 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Felicia Engmann ◽

Ferdinand Apietu Katsriku ◽

Jamal-Deen Abdulai ◽

Kofi Sarpong Adu-Manu

Keyword(s):

Data Transmission ◽

Energy Savings ◽

Data Driven ◽

Wireless Sensor ◽

Time Series Models ◽

Duty Cycling ◽

Data Driven Approach ◽

Save Energy ◽

The Cost ◽

Prediction Approach

Energy conservation is critical in the design of wireless sensor networks since it determines its lifetime. Reducing the frequency of transmission is one way of reducing the cost, but it must not tamper with the reliability of the data received at the sink. In this paper, duty cycling and data-driven approaches have been used together to influence the prediction approach used in reducing data transmission. While duty cycling ensures nodes that are inactive for longer periods to save energy, the data-driven approach ensures features of the data that are used in predicting the data that the network needs during such inactive periods. Using the grey series model, a modified rolling GM(1,1) is proposed to improve the prediction accuracy of the model. Simulations suggest a 150% energy savings while not compromising on the reliability of the data received.

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Extracting Value from Industrial Alarms and Events: A Data-Driven Approach Based on Exploratory Data Analysis

Sensors ◽

10.3390/s19122772 ◽

2019 ◽

Vol 19 (12) ◽

pp. 2772 ◽

Cited By ~ 3

Author(s):

Aguinaldo Bezerra ◽

Ivanovitch Silva ◽

Luiz Affonso Guedes ◽

Diego Silva ◽

Gustavo Leitão ◽

...

Keyword(s):

Big Data ◽

Data Analysis ◽

Data Exchange ◽

Data Science ◽

Exploratory Data Analysis ◽

Data Driven ◽

Industrial Data ◽

Industrial Big Data ◽

Data Driven Approach ◽

Exploratory Data

Alarm and event logs are an immense but latent source of knowledge commonly undervalued in industry. Though, the current massive data-exchange, high efficiency and strong competitiveness landscape, boosted by Industry 4.0 and IIoT (Industrial Internet of Things) paradigms, does not accommodate such a data misuse and demands more incisive approaches when analyzing industrial data. Advances in Data Science and Big Data (or more precisely, Industrial Big Data) have been enabling novel approaches in data analysis which can be great allies in extracting hitherto hidden information from plant operation data. Coping with that, this work proposes the use of Exploratory Data Analysis (EDA) as a promising data-driven approach to pave industrial alarm and event analysis. This approach proved to be fully able to increase industrial perception by extracting insights and valuable information from real-world industrial data without making prior assumptions.

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Sensor-Fault Detection, Isolation and Accommodation for Digital Twins via Modular Data-Driven Architecture

IEEE Sensors Journal ◽

10.1109/jsen.2020.3029459 ◽

2020 ◽

pp. 1-1

Author(s):

Hossein Darvishi ◽

Domenico Ciuonzo ◽

Eivind Roson Eide ◽

Pierluigi Salvo Rossi

Keyword(s):

Fault Detection ◽

Data Driven ◽

Sensor Fault ◽

Digital Twins ◽

Modular Data ◽

Sensor Fault Detection

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Challenges for the DOE methodology related to the introduction of Industry 4.0

Production Engineering Archives ◽

10.30657/pea.2020.26.33 ◽

2020 ◽

Vol 26 (4) ◽

pp. 190-194

Author(s):

Jacek Pietraszek ◽

Norbert Radek ◽

Andrii V. Goroshko

Keyword(s):

Big Data ◽

Data Analysis ◽

Data Integration ◽

Design Of Experiments ◽

Industry 4.0 ◽

Data Stream ◽

Data Driven ◽

Industrial Data ◽

Existing Problems

AbstractThe introduction of solutions conventionally called Industry 4.0 to the industry resulted in the need to make many changes in the traditional procedures of industrial data analysis based on the DOE (Design of Experiments) methodology. The increase in the number of controlled and observed factors considered, the intensity of the data stream and the size of the analyzed datasets revealed the shortcomings of the existing procedures. Modifying procedures by adapting Big Data solutions and data-driven methods is becoming an increasingly pressing need. The article presents the current methods of DOE, considers the existing problems caused by the introduction of mass automation and data integration under Industry 4.0, and indicates the most promising areas in which to look for possible problem solutions.

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