SENSOR PLATFORM OF INDUSTRIAL TOMOGRAPHY FOR DIAGNOSTICS AND CONTROL OF TECHNOLOGICAL PROCESSES

Krzysztof Król

krzysztof.krol@netrix.com.pl
1. Research and Development Center, Netrix S.A., 2. WSEI University (Poland)
http://orcid.org/0000-0002-0114-2794

Tomasz Rymarczyk


1. Research and Development Center, Netrix S.A., 2. WSEI University (Poland)
http://orcid.org/0000-0002-3524-9151

Konrad Niderla


1. Research and Development Center, Netrix S.A., 2. WSEI University (Poland)
http://orcid.org/0000-0003-1280-0622

Edward Kozłowski


Lublin University of Technology, Faculty of Management (Poland)
http://orcid.org/0000-0002-7147-4903

Abstract

This article presents an industrial tomography platform used to diagnose and control technological processes. The system has been prepared so that it is possible to add individual sensors cooperating with the system of an intelligent cyber-physical platform with an open architecture. Additionally, it is possible to configure and cooperate with external systems freely. As part of the experimental work, a platform has been developed that allows individual subsystems and external customer systems to work together. The cyber-physical system, a new generation of digital systems, focuses mainly on the complex interplay and integration between cyberspace and the physical world. A cyber-physical system consists of highly integrated computational, communication, control and physical elements. The solution focuses mainly on the complex interplay and integration between cyberspace and the physical world.


Keywords:

electrical capacitance tomography, cyber-physical systems, sensors, electrical impedance tomography

Akhtari S. et al.: Intelligent embedded load detection at the edge on industry 4.0 powertrains applications. 5th international forum on research and technology for society and industry – RTSD2019, 2019, 427–430.
DOI: https://doi.org/10.1109/RTSI.2019.8895598   Google Scholar

Assawaarayakul C. et al.: Integrate digital twin to exist production system for industry 4.0. 4th technology innovation management and engineering science international conference (TIMES-iCON) 2019, 1–5.
DOI: https://doi.org/10.1109/TIMES-iCON47539.2019.9024430   Google Scholar

Banasiak R. et al.: Study on two-phase flow regime visualisation and identification using 3D electrical capacitance tomography and fuzzy-logic classification. International Journal of Multiphase Flow 58, 2014, 1–14 [http://doi.org/10.1016/J.IJMULTIPHASEFLOW.2013.07.003].
DOI: https://doi.org/10.1016/j.ijmultiphaseflow.2013.07.003   Google Scholar

Daubechies I.: Orthonormal Bases of Compactly Supported Wavelets. Communications on Pure and Applied Mathematics 41(7), 1988, 909–96.
DOI: https://doi.org/10.1002/cpa.3160410705   Google Scholar

He J. et al.: Locality-aware replacement algorithm in flash memory to optimise cloud computing for smart factory of industry 4.0. IEEE Access 5, 2017, 16252–16262.
DOI: https://doi.org/10.1109/ACCESS.2017.2740327   Google Scholar

Hui Z., Hastie T.: Regularisation and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67(2), 2005, 301–20.
DOI: https://doi.org/10.1111/j.1467-9868.2005.00503.x   Google Scholar

Król K. et al.: Intelligent Sensor Platform with Open Architecture for Monitoring and Control of Industry 4.0 Systems. European Research Studies Journal 24(2), 2021, 597–606.
DOI: https://doi.org/10.35808/ersj/2288   Google Scholar

Kania K. et al.: Image reconstruction in ultrasound transmission tomography using the Fermat’s Principle. Przegląd Elektrotechniczny 96(1), 2020, 186–189.
DOI: https://doi.org/10.15199/48.2020.01.41   Google Scholar

Kłosowski G. et al.: Maintenance of industrial reactors supported by deep learning driven ultrasound tomography. Eksploatacja i Niezawodność – Maintenance and Reliability 22(1), 2020, 138–147 [http://doi.org/10.17531/ein.2020.1.16].
DOI: https://doi.org/10.17531/ein.2020.1.16   Google Scholar

Kłosowski G. et al.: Neural hybrid tomograph for monitoring industrial reactors. Przegląd Elektrotechniczny 97(12), 2020, 190–193.
DOI: https://doi.org/10.15199/48.2020.12.40   Google Scholar

Kłosowski G. et al.: Quality Assessment of the Neural Algorithms on the Example of EIT-UST Hybrid Tomography. Sensors 20, 2020 [http://doi.org/10.3390/s20113324].
DOI: https://doi.org/10.3390/s20113324   Google Scholar

Kłosowski G. et al.: Using an LSTM network to monitor industrial reactors using electrical capacitance and impedance tomography – a hybrid approach. Eksploatacja i Niezawodność – Maintenance and Reliability 25(1), 2023, 11 [http://doi.org/10.17531/ein.2023.1.11].
DOI: https://doi.org/10.17531/ein.2023.1.11   Google Scholar

Kong X. T. et al.: Cyber physical ecommerce logistics system: an implementation case in Hong Kong. Comput Ind Eng 139, 2020, 106170.
DOI: https://doi.org/10.1016/j.cie.2019.106170   Google Scholar

Kozłowski E. et al.: Logistic regression in image reconstruction in electrical impedance tomography. Przegląd Elektrotechniczny 97(5), 2020, 95–98.
DOI: https://doi.org/10.15199/48.2020.05.19   Google Scholar

Kozłowski E. et al.: The use of principal component analysis and logistic regression for cutter state identification. Innovations in Industrial Engineering, Springer International Publishing 2021, 396–405.
DOI: https://doi.org/10.1007/978-3-030-78170-5_34   Google Scholar

Kozłowski E. et al.: Application of the logistic regression for determining transition probability matrix of operating states in the transport systems. Eksploatacja i Niezawodność – Maintenance and Reliability, 22(2), 2020, 192–200 [http://doi.org/10.17531/ein.2020.2.2].
DOI: https://doi.org/10.17531/ein.2020.2.2   Google Scholar

Kozłowski E. et al.: Assessment model of cutting tool condition for real-time supervision system. Eksploatacja i Niezawodność – Maintenance and Reliability, 21(4), 2019, 679–685 [http://doi.org/10.17531/ein.2019.4.18].
DOI: https://doi.org/10.17531/ein.2019.4.18   Google Scholar

Lins T. et al.: Cyber-physical production systems retrofitting in context of industry 4.0. Comput Ind Eng 139, 2020, 106193, 59.
DOI: https://doi.org/10.1016/j.cie.2019.106193   Google Scholar

Manavalan E., Jayakrishna K.: A review of internet of things (iot) embedded sustainable supply chain for industry 4.0 requirements. Comput Ind Eng 127, 2019, 925–953.
DOI: https://doi.org/10.1016/j.cie.2018.11.030   Google Scholar

Occhiuzzi C. et al.: Rfid technology for industry 4.0: architectures and challenges. IEEE international conference on RFID technology and applications (RFID-TA) 2019, 181–186.
DOI: https://doi.org/10.1109/RFID-TA.2019.8892049   Google Scholar

Percival D. B, Walden A.: Wavelet Methods for Time Series Analysis 4. Cambridge University Press, 2000.
DOI: https://doi.org/10.1017/CBO9780511841040   Google Scholar

Poór P. et al.: Predictive maintenance 4.0 as next evolution step in industrial maintenance development. International Research Conference on Smart Computing and Systems Engineering – SCSE, 2019, 245–253.
DOI: https://doi.org/10.23919/SCSE.2019.8842659   Google Scholar

Rymarczyk T., Sikora J.: Optimisation Method and PCA noise suppression application for Ultrasound Transmission Tomography. Przegląd Elektrotechniczny 96(2), 2020, 90–93.
  Google Scholar

Rymarczyk T.: New methods to determine moisture areas by electrical impedance tomography. International Journal of Applied Electromagnetics and Mechanics 52(1–2), 2016, 79–87 [http://doi.org/10.3233/JAE-162071].
DOI: https://doi.org/10.3233/JAE-162071   Google Scholar

Rymarczyk T. et al.: Ultrasonic tomography for reflection and transmission wave analysis. Przegląd Elektrotechniczny 96(3), 2020, 170–173.
DOI: https://doi.org/10.15199/48.2020.03.37   Google Scholar

Rymarczyk T. et al.: The use of the autoencoder to improve images in ultrasound tomography. Przegląd Elektrotechniczny 96(8), 2020, 160–163.
DOI: https://doi.org/10.15199/48.2020.08.33   Google Scholar

Rymarczyk T. et al.: Logistic Regression for Machine Learning in Process Tomography. Sensors 19(15), 2020 [http://doi.org/10.3390/s19153400].
DOI: https://doi.org/10.3390/s19153400   Google Scholar

Rymarczyk T. et al.: Analysis of vertical and horizontal flows of liquids and gases through a wire-mesh sensor. Przegląd Elektrotechniczny 96(3), 2020.
DOI: https://doi.org/10.15199/48.2020.03.38   Google Scholar

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Published
2023-03-31

Cited by

Król, K., Rymarczyk, T., Niderla, K., & Kozłowski, E. (2023). SENSOR PLATFORM OF INDUSTRIAL TOMOGRAPHY FOR DIAGNOSTICS AND CONTROL OF TECHNOLOGICAL PROCESSES. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 13(1), 33–37. https://doi.org/10.35784/iapgos.3371

Authors

Krzysztof Król 
krzysztof.krol@netrix.com.pl
1. Research and Development Center, Netrix S.A., 2. WSEI University Poland
http://orcid.org/0000-0002-0114-2794

Authors

Tomasz Rymarczyk 

1. Research and Development Center, Netrix S.A., 2. WSEI University Poland
http://orcid.org/0000-0002-3524-9151

Authors

Konrad Niderla 

1. Research and Development Center, Netrix S.A., 2. WSEI University Poland
http://orcid.org/0000-0003-1280-0622

Authors

Edward Kozłowski 

Lublin University of Technology, Faculty of Management Poland
http://orcid.org/0000-0002-7147-4903

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