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Knowledge-based power monitoring and fault prediction system for smart factories

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Abstract

With the recent spread of the 4th Industrial Revolution, the intellectualization of industry is progressing rapidly. In particular, companies in various field are interested in converting existing factories into smart factory, and the number of cases where the smart factory template is applied is increasing. In this paper, we design and implement an IoT-based power monitoring and data collection system that enables monitoring of power consumption as well as the detection of abnormal power consumption in a smart factory. The system consists of power measurement devices, data analysis servers, and knowledge-based web and smartphone applications. The power measurement device uses IoT sensors to measure power consumption and sends collected data to the server. The server analyzes the data collected from the device using R and exploits the analysis results to provide predictions about the failure of equipment and facilities in the smart factory. From this point of view, we can expect improvement in not only cost-efficiency but also product quality.

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References

  1. Hofmann E, Rüsch M (2017) Industry 4.0 and the current status as well as future prospects on logistics. Comput Ind 89:23–34, University of St. Gallen, Switzerland

    Article  Google Scholar 

  2. Wang S, Wan J, Li D, Zhang C (2016) Implementing smart factory of Industrie 4.0: an outlook. International Journal of Distributed Sensor Networks 12, 2, China

  3. Shrouf F, Ordieres J, Miragliotta G (2014) Smart factories in Industry 4.0: a review of the concept and of energy management approached in production based on the Internet of Things paradigm. IEEE International Conference on Industrial Engineering and Engineering Management, Bandar Sunway, Malaysia, pp. 697-701

  4. Wang S, Wan J, Zhang D, Li D, Zhang C (2014) Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput Netw 101:158–168

    Article  Google Scholar 

  5. Frazzon EM, Hartmann J, Makuschewitz T, Scholz-Reiter B (2013) towards socio-cyber-physical systems in production networks. Procedia CIRP 7:49–54

    Article  Google Scholar 

  6. Jin-Hee K (2017) A study on the platform for big data analysis of manufacturing process. Journal of Convergence for Information Technology 7, 5, Rep. of Korea

  7. Kim J-Y, Kim S-Y (2017) Design and implementation of the XML-based FEMS for flexible energy management system construction in various manufacturing plants. The Korean Institute of Information Scientists and Engineers:1547–1549

  8. Roland Berger Strategy Consultants (2014) THINK ACT INDUSTRY 4.0 - The new industrial revolution How Europe will succeed: 1–24

  9. Understanding Boxplots, https://towardsdatascience.com/understanding-boxplots-5e2df7bcbd51. Accessed 12-01-2019

  10. Andrew Kusiak, Wenyan Li, “The prediction and diagnosis of wind turbine faults”, Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, USA, 2010.

  11. Baek SH (2016) The-state-of-the art and standardization strategies for smart manufacturing. KISTEP Issue Paper 2016-03

  12. Monostori L, Vancza J, Kumara SRT (2006) Agent-based systems for manufacturing. CIRP Annals–Manufacturing Technology 55(2):697–720

    Article  Google Scholar 

  13. Rabaey JM, Ammer J, Karalar T, Li S, Otis B, Sheets M, Tuan T (2002) PicoRadios for wireless sensor networks: the next challenge in ultra-low power design. ISSCC, San Francisco

    Google Scholar 

  14. Uraisami K (2018) Business planning on efficiency, productivity, and profitability. Springer Nature, Singapore Pte Ltd, pp 83–118

    Google Scholar 

  15. Kozai T (2018) Designing a cultivation system module (csm) considering the cost performance: a step smart PFALs. Springer Nature, Singapore Pte, pp 57–80

    Google Scholar 

  16. Nakabo Y (2018) Design and control of smart plant factory. Springer Nature, Singarpore Pte Ltd, pp 51–55

    Google Scholar 

  17. Claude J, Strimmer K (2004) APE: analyses of phylogenetics evolution in R language. Bioinformatics 20(2):289–290

    Article  Google Scholar 

  18. Hababeh I, Thabain A, Alouneh S (2019) An effective multivariate control framework for monitoring cloud systems performance. KSII Transactions on Internet and Information Systems 13(1):86–109

    Google Scholar 

Download references

Funding

This research was financially supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03032777), and this work was supported by the Soonchunhyang University Research Fund.

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Correspondence to Seokhoon Kim.

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Kim, E., Huh, DH. & Kim, S. Knowledge-based power monitoring and fault prediction system for smart factories. Pers Ubiquit Comput 26, 307–318 (2022). https://doi.org/10.1007/s00779-019-01348-4

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