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Application of Artificial Neural Networks in Condition Based Predictive Maintenance

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Recent Developments in Intelligent Information and Database Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 642))

Abstract

This paper reviews different techniques of maintenance, artificial neural networks (ANN) and their various applications in fault risk assessment and an early fault detection analysis. The predictive maintenance is in focus of production facilities supplying in long supplier chains of automotive industry to ensure the reliable and continuous production and on-time deliveries. ANN offer a powerful tool to evaluate machine data and parameters which can learn from process data of fault simulation. Finally there are reviewed examples of usage of ANN in specific predictive maintenance cases.

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References

  1. Hines, P., Holweg, M., Rich, N.: Learning to evolve: a review of contemporary lean thinking. Int. J. Oper. Prod. Manag. 24(10), 994–1011 (2004)

    Article  Google Scholar 

  2. Naylor, J.B., Naim, M.M., Berry, D.: Leagility: integrating the lean and agile manufacturing paradigms in the total supply chain. Int. J. Prod. Econ. 62(1), 107–118 (1999)

    Article  Google Scholar 

  3. Abdulmalek, F.A., Rajgopal, J.: Analyzing the benefits of lean manufacturing and value stream mapping via simulation: a process sector case study. Int. J. Prod. Econ. 107, 223–236 (2007)

    Article  Google Scholar 

  4. Wu, S.J., Gebraeel, N., Lawley, M.A., Yih, Y.: A neural network integrated decision support system for condition-based optimal predictive maintenance policy. Man Cybern. Part A: Syst. Hum. IEEE Trans. Syst. 37(2), 226–236 (2007)

    Article  Google Scholar 

  5. Shin, J.H., Jun, H.B.: On condition based maintenance policy. J. Comput. Des. Eng. (2015)

    Google Scholar 

  6. Yegnanarayana, B.: Artificial neural networks. PHI Learning Pvt, Delhi (2009)

    Google Scholar 

  7. Maltarollo, V.G., da Silva, A.B.F., Honório, K.M.: Applications of Artificial Neural Networks in Chemical Problems. INTECH Open Access Publisher (2013)

    Google Scholar 

  8. Krenek, J., Kuca, K., Krejcar, O., Maresova, P., Sobeslav, V., Blazek, P.: Artificial neural network tools for computerised data modelling and processing. In: 15th IEEE International Symposium on Computational Intelligence And Informatics, pp. 255–260 (2014)

    Google Scholar 

  9. Cheng, F., Sutariya, V.: Applications of artificial neural network modeling in drug discovery. Clin. Exp. Pharmacol. 2(3), 1–2 (2012)

    Article  Google Scholar 

  10. Mendyk, A., Tuszyński, P.K., Polak, S., Jachowicz, R.: Generalized in vitro-in vivo relationship (IVIVR) model based on artificial neural networks. Drug Des. Dev. Ther. 7, 223 (2013)

    Google Scholar 

  11. Krenek, J., Kuca, K., Bartuskova, A., Krejcar, O., Maresova, P., Sobeslav, V.: Artificial neural networks in biomedicine applications. In: Proceedings of the 4th International Conference on Computer Engineering and Networks, Springer, pp. 133–139 (2015)

    Google Scholar 

  12. Motalleb, G.: Artificial neural network analysis in preclinical breast cancer. Cell J. (Yakhteh) 15(4), 324 (2014)

    Google Scholar 

  13. Samanta, R.K., Mitra, M.: A Neural Network Based Intelligent System for Breast Cancer Diagnosis (2013)

    Google Scholar 

  14. Jancikova, Z., Zimny, O., Kostial, P.: Prediction of metal corrosion by neural networks. Metalurgija 3(52), 379–381 (2013)

    Google Scholar 

  15. Krejcar, O., Frischer, R.: Non destructive defect detection by spectral density analysis. Sensors 11(3), 2334–2346 (2011)

    Article  Google Scholar 

  16. Machacek, Z., Hajovsky, R.: Modeling of temperature field distribution in mine dumps with spread prediction. Elektron. Ir Elektrotechnika 19(7), 53–56 (2013)

    Article  Google Scholar 

  17. Meireles, M.R.G., Almeida, P.E.M., Simoes, M.G.: A comprehensive review for industrial applicability of artificial neural networks. IEEE Trans. Industr. Electron. 50(3), 585–601 (2003)

    Article  Google Scholar 

  18. Sun, F., Gao, L., Zou, J., Wu, T., Li, J.: Study on multi-equipment failure prediction based on system network. Sens. Transducers 158(11), 427–435 (2013)

    Google Scholar 

  19. Villanueva, J.B., Espadafor, F.J., Cruz-Peragon, F., García, M.T.: A methodology for cracks identification in large crankshafts. Mech. Syst. Sig. Process. 25(8), 3168–3185 (2011)

    Article  Google Scholar 

  20. Castejón, C., Lara, O., García-Prada, J.C.: Automated diagnosis of rolling bearings using MRA and neural networks. Mech. Syst. Signal Process. 24, 289–299 (2009)

    Article  Google Scholar 

  21. Saxena, A., Saad, A.: Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems. Appl. Soft Comput. 7(1), 441–454 (2007)

    Article  Google Scholar 

  22. Pacheco, W.S., Castro Pinto, F.A.N.: Classifier based on artificial neural networks and beamforming technique for bearing fault detection. In: ABCM, 22nd International Congress of Mechanical Engineering COBEM, Brazil (2013)

    Google Scholar 

  23. Unal, M., Demetgul, M., Onat, M., Kucuk, H.: Fault diagnosis of rolling bearing based on feature extraction and neural network algorithm. Recent Adv. Electr. Eng. Ser. 10 (2013)

    Google Scholar 

  24. Rao, B.K.N., Srinivasa, P., Nagabhushana, T.N.: Failure diagnosis and prognosis of rolling—element bearings using artificial neural networks: a critical overview. J. Phys.: Conf. ser. 364, 1–28 (2012)

    Google Scholar 

  25. Tian, Z.: An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring. J. Intell. Manuf. 23(2), 227–237 (2012)

    Article  Google Scholar 

  26. Al-Kassir, A.R., Fernandez, J., Tinaut, F.V., Castro, F.: Thermographic study of energetic installations. Appl. Therm. Eng. 25(2), 183–190 (2005)

    Article  Google Scholar 

  27. Meola, C., Carlomagno, G.M.: Recent advances in the use of infrared thermography. Meas. Sci. Technol. 15(9), R27 (2004)

    Article  Google Scholar 

  28. Lizák, F., Kolcun, M.: Improving reliability and decreasing losses of electrical system with infrared thermography. Acta Electrotechnica et Informatica 8(1), 60–63 (2008)

    Google Scholar 

  29. Nazmul Huda, A.S., Taib, S.: Application of infrared thermography for predictive/preventive maintenance of thermal defect in electrical equipment. Appl. Therm. Eng. 61, 220–227 (2013)

    Article  Google Scholar 

  30. Demetgul, M., Tansel, I.N., Taskin, S.: Fault diagnosis of pneumatic systems with artificial neural network algorithms. Experts Syst. Appl. 36, 10512–10519 (2009)

    Article  Google Scholar 

  31. Eski, I., Erkaya, S., Setrac, S., Yildirim, S.: Fault detection on robot manipulators using artificial neural networks. Robot. Comput-Integr. Manufact. 27, 115–123 (2011)

    Article  Google Scholar 

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Acknowledgements

This work and the contribution were also supported by project “Smart Solutions for Ubiquitous Computing Environments” FIM, University of Hradec Kralove, Czech Republic (under ID: UHK-FIM-SP-2016).

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Authors and Affiliations

  1. Center for Basic and Applied Research Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic

    Jiri Krenek, Kamil Kuca, Pavel Blazek & Ondrej Krejcar

  2. Faculty of Military Health Sciences, University of Defence Hradec Kralove, Hradec Kralove, Czech Republic

    Pavel Blazek & Daniel Jun

Authors
  1. Jiri Krenek
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  2. Kamil Kuca
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  3. Pavel Blazek
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  4. Ondrej Krejcar
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  5. Daniel Jun
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Corresponding author

Correspondence to Jiri Krenek .

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Editors and Affiliations

  1. Faculty of Computer Science and Manageme, Wrocław University of Technology, Wrocław, Poland

    Dariusz Król

  2. Faculty of Computer Science and Manageme, Wrocław University of Technology, Wrocław, Poland

    Lech Madeyski

  3. Faculty of Computer Science and Manageme, Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

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Krenek, J., Kuca, K., Blazek, P., Krejcar, O., Jun, D. (2016). Application of Artificial Neural Networks in Condition Based Predictive Maintenance. In: Król, D., Madeyski, L., Nguyen, N. (eds) Recent Developments in Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 642. Springer, Cham. https://doi.org/10.1007/978-3-319-31277-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-31277-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31276-7

  • Online ISBN: 978-3-319-31277-4

  • eBook Packages: EngineeringEngineering (R0)

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