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A Distributed Platform for Cycle Detection and Analysis in Cyber-Physical Systems

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Advanced Information Networking and Applications (AINA 2024)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 200))

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Abstract

This paper presents a distributed platform that collects and processes data streams from Industrial IoT/Cyber-Physical Systems. We demonstrate the design and performance of our platform with an emphasis on accuracy and scalability. We validate our platform using three use cases: production cycle detection, cycle analysis, and anomaly detection.

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Notes

  1. 1.

    Advanced computational statistics for planning and tracking production environments.

  2. 2.

    https://www.iiconsortium.org/.

  3. 3.

    https://www.iiconsortium.org/patterns/.

  4. 4.

    https://learn.microsoft.com/en-us/azure/architecture/solution-ideas/articles/iot-predictive-maintenance.

  5. 5.

    https://scikit-learn.org/stable/modules/classes.html.

  6. 6.

    https://web.eece.maine.edu/~vweaver/projects/rapl/.

  7. 7.

    https://numpy.org/.

References

  1. Khan, W.Z., Rehman, M.H., Zangoti, H.M., Afzal, M.K., Armi, N., Salah, K.: Industrial internet of things: recent advances, enabling technologies and open challenges. Comput. Electr. Eng. 81, 106522 (2020)

    Article  Google Scholar 

  2. Lydon, B.: RAMI 4.0 reference architectural model for Industrie 4.0. InTech, April 2019

    Google Scholar 

  3. Reference architecture model Industrie 4.0 (RAMI4.0). Technical report DIN SPEC 91345:2016-04, Deutsches Institut fur Normung E.V. (DIN) (2016)

    Google Scholar 

  4. The industrial internet reference architecture. Technical report, Industry IoT Consortium (2022)

    Google Scholar 

  5. Cosner, M., Garcia, A.B.: Azure IoT reference architecture. White paper, Microsoft (2023)

    Google Scholar 

  6. Sodabathina, R., Shan, J., Ulloa, M.: Building event-driven architectures with IoT sensor data. AWS architecture blog, Amazon Web Services (2022)

    Google Scholar 

  7. Eclipse IoT Working Group. The three software stacks required for IoT architectures. White paper (2016)

    Google Scholar 

  8. Keith Mobley, R.: Introduction to Predictive Maintenance. Plant Engineering, 2nd edn. Elsevier Science & Technology, Oxford (2002). Description based on publisher supplied metadata and other sources

    Google Scholar 

  9. Passlick, J., Dreyer, S., Olivotti, D., Grützner, L., Eilers, D., Breitner, M.H.: Predictive maintenance as an internet of things enabled business model: a taxonomy. Electron. Mark. 31(1), 67–87 (2020)

    Article  Google Scholar 

  10. Carvalho, T.P., Soares, F.A.A.M.N., Vita, R., Francisco, R.P., Basto, J.P., Alcalá, S.G.S.: A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 137, 106024 (2019)

    Google Scholar 

  11. Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E., Loncarski, J.: Machine learning approach for predictive maintenance in industry 4.0. In: 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), July 2018. IEEE (2018)

    Google Scholar 

  12. Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., Elger, G.: Predictive maintenance enabled by machine learning: use cases and challenges in the automotive industry. Reliab. Eng. Syst. Saf. 215, 107864 (2021)

    Article  Google Scholar 

  13. Ayvaz, S., Alpay, K.: Predictive maintenance system for production lines in manufacturing: a machine learning approach using IoT data in real-time. Exp. Syst. Appl. 173, 114598 (2021)

    Article  Google Scholar 

  14. Iuhasz, G., Panica, S., Duma, A.: Cycle detection and clustering for cyber physical systems. In: Barolli, L. (eds.) Advanced Information Networking and Applications, AINA 2023. LNNS, vol. 655, pp. 100–114. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28694-0_10

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Acknowledgement

The Romania Competitiveness Operational Programme partially supports this paper under project number SMIS 120725 - SCAMP-ML (Advanced computational statistics for planning and tracking production environments) and UEFISCDI COCO research project PN III-P4-ID-PCE-2020-0407.

Author information

Authors and Affiliations

  1. West University of Timioara, 300223, Timioara, Romania

    Gabriel Iuhasz & Florin Fortis

  2. Research Institute e-Austria Timioara, 300223, Timioara, Romania

    Gabriel Iuhasz, Silviu Panica & Florin Fortis

  3. Innovation Qube SRL, Timisoara, Romania

    Silviu Panica

  4. ETA2U Computers, Gh. Dima Nr 1, Timisoara, Romania

    Alecsandru Duma

Authors
  1. Gabriel Iuhasz
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  2. Silviu Panica
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  3. Florin Fortis
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  4. Alecsandru Duma
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Corresponding author

Correspondence to Gabriel Iuhasz .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

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Iuhasz, G., Panica, S., Fortis, F., Duma, A. (2024). A Distributed Platform for Cycle Detection and Analysis in Cyber-Physical Systems. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 200. Springer, Cham. https://doi.org/10.1007/978-3-031-57853-3_37

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