Abstract
The machine learning techniques can be efficiently used for optimal maintenance decision making. Currently, most of the companies and manufactures possess huge amounts of sensor, process, and environment data. Combining the data with the information about the failures succeeds in creating useful train data sets for predictive maintenance purposes. In this paper, we propose the approach of efficient data processing in order to maximize the predictive quality of machine learning models. We investigate numerous machine-learning methods and propose the procedure to automatize the predictive maintenance process. The results obtained for the real data were satisfactory and applicable.
Reliability Solutions sp. z o.o., Krakow, Poland.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mobley, R.: An Introduction to Predictive Maintenance, 2nd edn. Butterworth-Heinemann, Oxford (2002)
Scheffer, C., Girdhar, P.: Practical Machinery Vibration Analysis and Predictive Maintenance. Elsevier, Amsterdam (2004)
Nicholas, J.R., Young, R.K.: Predictive Maintenance Management, 1st edn. Maintenance Quality Systems LLC (2003)
Ketchen Jr., D.J., Shook, C.L.: The application of cluster analysis in strategic management research: an analysis and critique. Strateg. Manag. J. 17(6), 441–459 (1996)
Hassanat A., Abbadi M., Altarawneh G.: Solving the Problem of the K Parameter in the KNN Classifier Using an Ensemble Learning Approach. In: IJCSIS, vol 12, No 8 (2014)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Ran S., Cao X., Wei Y., Sun Y.: Global Refinement of Random Forest (CVPR 2015)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Agresti, A.: Categorical Data Analysis, 3rd edn. John Wiley & Sons Inc, New Jersey (2013)
Baker, G.A.: Transformation of non-normal frequency distributions into normal distributions. Ann. Math. Stat. 5, 113–123 (1934)
Bartlett, M.S.: The use of transformation. Biom. Bullet. 3, 39–52 (1947)
Box, G.E.P., Cox, D.R.: An analysis of transformations. JR Stat. Soc. B. 26(2), 211–252 (1964)
Conway, D., White, J.M.: Machine Learning for Hackers. Case Studies and Algorithms to Get You Started. O’Reilly Media, Sebastopol (2012)
Finney, D.J.: Transformation of frequency distributions. Nat. Lond. 162, 898 (1948)
Marzec, M., Uhl, T., Michalak, D.: Verification of text mining techniques accuracy when dealing with urban buses maintenance data. Diagnostyka 15, 51–57 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Marzec, M., Morkisz, P., Wojdyła, J., Uhl, T. (2018). Intelligent Predictive Maintenance System. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-56994-9_55
Download citation
DOI: https://doi.org/10.1007/978-3-319-56994-9_55
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56993-2
Online ISBN: 978-3-319-56994-9
eBook Packages: EngineeringEngineering (R0)