ANALYSIS OF DATA FROM MEASURING SENSORS FOR PREDICTION IN PRODUCTION PROCESS CONTROL SYSTEMS

Tomasz Rymarczyk

tomasz@rymarczyk.com
1. Research & Development Centre Netrix SA; 2. University of Economics and Innovation in Lublin (Poland)
http://orcid.org/0000-0002-3524-9151

Bartek Przysucha


Lublin University of Technology (Poland)
http://orcid.org/0000-0002-1117-8088

Marcin Kowalski


University of Economics and Innovation in Lublin (Poland)
https://orcid.org/0000-0002-1644-0612

Piotr Bednarczuk


University of Economics and Innovation in Lublin (Poland)
https://orcid.org/0000-0003-1933-7183

Abstract

The article presents a solution based on a cyber-physical system in which data collected from measuring sensors was analysed for prediction in the production process control system. The presented technology was based on intelligent sensors as part of the solution for Industry 4.0. The main purpose of the work is to reduce data and select the appropriate covariate to optimise modelling of defects using the Cox model for a specific mechanical system. The reliability of machines and devices in the production process is a condition for ensuring continuity of production. Predicting damage, especially its movement, gives the ability to monitor the current state of the machine. In a broader perspective, this enables streamlining the production process, service planning or control. This ensures production continuity and optimal performance. The presented model is a regressive survival analysis model that allows you to calculate the probability of failure occurring over a given period of time.


Keywords:

Cox Model, Time to Failure Prediction, Production Control, Intelligent Platform

Bergweiler S.: Intelligent Manufacturing based on Self-Monitoring Cyber-Physical Systems. UBICOMM 2015 The Ninth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, 2015.
  Google Scholar

Chen B., Abascal J., Soleimani M.: Electrical Resistance Tomography for Visualization of Moving Objects Using a Spatiotemporal Total Variation Regularization Algorithm. Sensors 18/2018, 1704.
DOI: https://doi.org/10.3390/s18061704   Google Scholar

Cox D., Snell E.: Ageneral definition of residuals. Journal of the Royal Statistical Society Series B (Methodological) 30/1968, 248–275.
DOI: https://doi.org/10.1111/j.2517-6161.1968.tb00724.x   Google Scholar

Deszyńska A.: Modele hazardów proporcjonalnych Coxa. Matematyka stosowana 13(54)/2011.
  Google Scholar

Dušek J., Hladký D., Mikulka J.: Electrical Impedance Tomography Methods and Algorithms Processed with a GPU. PIERS Proceedings 2017, 1710–1714.
DOI: https://doi.org/10.1109/PIERS.2017.8262025   Google Scholar

Goetzke-Pala A., Hoła A., Sadowski Ł.: A non-destructive method of the evaluation of the moisture in saline brick walls using artificial neural networks. Archives of Civil and Mechanical Engineering 18(4)/2018, 1729–1742.
DOI: https://doi.org/10.1016/j.acme.2018.07.004   Google Scholar

Grudzien K., Romanowski A., Chaniecki Z., Niedostatkiewicz M., Sankowski D.: Description of the silo flow and bulk solid pulsation detection using ECT. Flow Measurement and Instrumentation 21(3)/2010, 198–206.
DOI: https://doi.org/10.1016/j.flowmeasinst.2009.12.006   Google Scholar

Kozlowski E., Mazurkiewicz D., Kowalska B., et al.: Binary Linear Programming as a Decision-Making Aid for Water Intake Operators. 1st International Conference on Intelligent Systems in Production Engineering and Maintenance (ISPEM), Wrocław 2017.
DOI: https://doi.org/10.1007/978-3-319-64465-3_20   Google Scholar

Korzeniewska E., Walczak M., Rymaszewski J.: Elements of Elastic Electronics Created on Textile Substrate. Proceedings of the 24th International Conference Mixed Design of Integrated Circuits and Systems – MIXDES 2017, 2017, 447–454.
DOI: https://doi.org/10.23919/MIXDES.2017.8005250   Google Scholar

Kowalska A., Banasiak R., Romanowski A., Sankowski D.: Article 3D-Printed Multilayer Sensor Structure for Electrical Capacitance Tomography. Sensors 19/2019, 3416.
DOI: https://doi.org/10.3390/s19153416   Google Scholar

Kryszyn J., Smolik W.: Toolbox for 3d modelling and image reconstruction in electrical capacitance tomography. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska – IAPGOŚ 7(1)/2017, 137–145.
  Google Scholar

Kozłowski E., Mazurkiewicz D., Żabiński T., Prucnal S., Sęp J.: Assessment model of cutting tool condition for real-time supervision system. Eksploatacja i Niezawodność – Maintenance and Reliability 21(4)/2019, 679–685.
DOI: https://doi.org/10.17531/ein.2019.4.18   Google Scholar

Monostori L. Cyber-physical production systems: Roots, expectations and R&D challenges. Procedia CIRP 17, 2014, 9–13.
DOI: https://doi.org/10.1016/j.procir.2014.03.115   Google Scholar

Mosorov V., Grudzień K., Sankowski D.: Flow velocity measurement methods using electrical capacitance tomography. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska – IAPGOŚ, 7(1)/2017, 30–36.
DOI: https://doi.org/10.5604/01.3001.0010.4578   Google Scholar

Qian F., Xu G., Zhang L., Dong H.: Design of Hybrid NC Control System for Automatic Line. International Journal of Hybrid Information Technology 8(4)/2015, 185–192.
DOI: https://doi.org/10.14257/ijhit.2015.8.4.21   Google Scholar

Repta D., Sacala I., Moisescu M., Stanescu A.: Towards the development of a Cyber-Intelligent Enterprise System Architecture. 19th World Congress The International Federation of Automatic Control, Cape Town 2014.
DOI: https://doi.org/10.3182/20140824-6-ZA-1003.01681   Google Scholar

Rymarczyk, T., Przysucha, B.: Intelligent sensor platform for multi-source data analysis for monitoring and control of technological systems. Applications of Electromagnetics in Modern Engineering and Medicine, PTZE 2019, 171–175.
DOI: https://doi.org/10.23919/PTZE.2019.8781710   Google Scholar

Rymarczyk T., Filipowicz S.F., Sikora J.: Level Set Method for Inverse Problem Solution In Electrical Impedance Tomography. Journal Proceedings of the XII International Conference on Electrical Bioimpedance & V Electrical Impedance Tomography, 2004, 519–522.
  Google Scholar

Rymarczyk T., Kłosowski G.: Innovative methods of neural reconstruction for tomographic images in maintenance of tank industrial reactors. Eksploatacja i Niezawodność – Maintenance and Reliability 21(2)/2019, 261–267.
DOI: https://doi.org/10.17531/ein.2019.2.10   Google Scholar

Rymarczyk T., Kozłowski E., Kłosowski G., Niderla K.: Logistic Regression for Machine Learning in Process Tomography. Sensors 19/2019, 3400.
DOI: https://doi.org/10.3390/s19153400   Google Scholar

Rymarczyk T.: Characterization of the shape of unknown objects by inverse numerical methods. Przegląd Elektrotechniczny 88(7b)/2012, 138–140.
  Google Scholar

Rymarczyk T., Adamkiewicz P., Polakowski K., Sikora J.: Effective ultrasound and radio tomography imaging algorithm for two-dimensional problems. Przegląd Elektrotechniczny 94(6)/2018, 62–69.
  Google Scholar

Rymarczyk T., Szumowski K., Adamkiewicz P., Tchórzewski P., Sikora J.: Moisture Wall Inspection Using Electrical Tomography Measurements. Przegląd Elektrotechniczny 94/2018, 97–100.
  Google Scholar

Schoenfeld D.: Partial residuals for the proportional hazards regression model, Biometrika 69/1980, 239–241.
DOI: https://doi.org/10.1093/biomet/69.1.239   Google Scholar

Xue Y., Schifano E. D.: Diagnostic for Cox model, Communications for statistical Applications and Methods 24(6)/2017, 583–604.
DOI: https://doi.org/10.29220/CSAM.2017.24.6.583   Google Scholar

Download


Published
2019-12-15

Cited by

Rymarczyk, T., Przysucha, B., Kowalski, M., & Bednarczuk, P. (2019). ANALYSIS OF DATA FROM MEASURING SENSORS FOR PREDICTION IN PRODUCTION PROCESS CONTROL SYSTEMS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 9(4), 26–29. https://doi.org/10.35784/iapgos.570

Authors

Tomasz Rymarczyk 
tomasz@rymarczyk.com
1. Research & Development Centre Netrix SA; 2. University of Economics and Innovation in Lublin Poland
http://orcid.org/0000-0002-3524-9151

Authors

Bartek Przysucha 

Lublin University of Technology Poland
http://orcid.org/0000-0002-1117-8088

Authors

Marcin Kowalski 

University of Economics and Innovation in Lublin Poland
https://orcid.org/0000-0002-1644-0612

Authors

Piotr Bednarczuk 

University of Economics and Innovation in Lublin Poland
https://orcid.org/0000-0003-1933-7183

Statistics

Abstract views: 394
PDF downloads: 211


Most read articles by the same author(s)

<< < 1 2 3 4