Abstract
Increasing the products service life and reducing the number of service visits are becoming more and more top priorities for Original Equipment Manufacturer companies (OEM’s). Condition-based maintenance is often proposed as a solution to reach this goal. However, this latter, is often hampered by the lack of the right information that gives a good indication of the health of the equipment. Furthermore, the processing power needed to compute this information is often not afforded by machine’s processor. In this paper, a remote platform which connects the OEM’s to the customer’s premises is described, allowing thus a local computation of available information. Two approaches are then combined to process the optimal maintenance time. First, data mining techniques and reliability estimation are applied to historical databases of machines running in the field in order to extract the relevant features together with their associated thresholds. Second, prediction algorithm is applied to the selected features in order to estimate the optimal time to preventively perform a maintenance action. The proposed method has been applied to a database of more than 2000 copy machines running in the field and proved to identify easily the relevant features to be forecasted and to offer an accurate prediction of the maintenance action.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
J. Blair & A. Shirkhodaie, (2001) Diagnosis and prognosis of bearings using data mining and numerical visualization techniques, Proceedings of the 33rd Southeastern Symposium on System theory, 395-399.
R. K. Mobley, (1990) An introduction to predictive maintenance, Van Nostrand Reinhold.
G. Van Dijck, (2008) Information theoretic approach to feature selection and redundancy assessment, PhD thesis, Katholieke Universiteit Leuven.
R. Isermann, (2006) Fault-diagnosis systems, Springer.
C. Shearer, (2000) The CRISP-DM model: the new blue print for data mining, Journal of data warehousing, 5(4), 13-22.
K. M. Goh, T. Tjahjono & S. Subramaniam, (2006) A review of research in manufacturing prognostics, IEEE International Conference on Industrial Informatics, 417-422.
R. Duda, P. Hart & D. Stork, (2001) Pattern classification, John Wiley & Sons, Inc.
W. Sholom, I. Nitin, (1998) Predictive data mining: a practical guide, Morgan Kaufmann.
P. Geurts, (2002) Contributions to decision tree induction, PhD thesis, University of Liège.
J. You, L. & S. Olafsson, (2006) Multi-attribute decision trees and decision rules, Chap. 10, Springer, Heidelberg, Germany, 327-358.
Y. Zhan, H. Chen, G. Zhang, (2006) An optimization algorithm of K-NN classification, Proceeding of the fifth international conference on machine learning and cybernetics, Dalian, 2246-2251.
P. Yadav, N. Choudhary, C. Bilen, (2008) Complex system reliability estimation methodology in the absence of failure data, Quality and reliability engineering international, 24, 745-764.
D. Komo, C. J. Cheng & H. Ko, (1994) Neural network technology for stock market index prediction, International symposium on speech, image processing and neural networks, Hong Kong, 534-546.
R. G. Brown, F. R. Meyer, (1961) The fundamental theorem of exponential smoothing, A. D. Little, Cambridge, 673-685.
E. S. Gardner, (2006) Exponential smoothing: the state of the arts – Part II, International journal of forecasting 22, 637- 666.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag
About this paper
Cite this paper
Bey-Temsamani, A., Engels, M., Motten, A., Vandenplas, S., Ompusunggu, A. (2010). Condition-based maintenance for OEM’s by application of data mining and prediction techniques. In: Kiritsis, D., Emmanouilidis, C., Koronios, A., Mathew, J. (eds) Engineering Asset Lifecycle Management. Springer, London. https://doi.org/10.1007/978-0-85729-320-6_62
Download citation
DOI: https://doi.org/10.1007/978-0-85729-320-6_62
Publisher Name: Springer, London
Print ISBN: 978-0-85729-321-3
Online ISBN: 978-0-85729-320-6
eBook Packages: EngineeringEngineering (R0)