Abstract
This work describes a new on-line sensor that includes a novel calibration process for the real-time condition monitoring of lubricating oil. The parameter studied with this sensor has been the variation of the Total Acid Number (TAN) since the beginning of oil’s operation, which is one of the most important laboratory parameters used to determine the degradation status of lubricating oil. The calibration of the sensor has been done using machine learning methods with the aim to obtain a robust predictive model. The methods used are ensembles of regression trees. Ensembles are combinations of models that often are able to improve the results of individual models. In this work the individual models were regression trees. Several ensemble methods were studied, the best results were obtained with Rotation Forests.
This work was supported by the vehicle interior manufacturer, Grupo Antolin Ingenieria S.A., within the framework of the project MAGNO2008 - 1028.- CENIT Project funded by the Spanish Ministry of Science and Innovation.
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References
Noria Corporation: What the tests tell us, http://www.machinerylubrication.com/Read/873/oil-tests
Gorritxategi, E., Arnaiz, A., Spiesen, J.: Marine oil monitoring by means of on-line sensors. In: Proceedings of MARTECH 2007- 2nd International Workshop on Marine Technology, Barcelona, Spain (2007)
Holmberg, H., Adgar, A., Arnaiz, A., Jantunen, E., Mascolo, J., Mekid, J.: E-maintenance. Springer, London (2010)
Yan, X., Zhao, C., Lu, Z.Y., Zhou, X., Xiao, H.: A study of information technology used in oil monitoring. Tribology International 38(10), 879–886 (2005)
Cho, S., Binsaeid, S., Asfour, S.: Design of multisensor fusion-based tool condition monitoring system in end milling. International Journal of Advanced Manufacturing Technology 46, 681–694 (2010)
Binsaeid, S., Asfour, S., Cho, S., Onar, A.: Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion. Journal of Materials Processing Technology 209(10), 4728–4738 (2009)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. SIGKDD Explorations 11(1) (2009)
Terradillos, J., Aranzabe, A., Arnaiz, A., Gorritxategi, E., Aranzabe, E.: Novel method for lube quality status assessment base on-visible spectrometric analysis. In: Proceedings of the International Congress Lubrication Management and Technology LUBMAT 2008, San Sebastian, Spain (2008)
Gorritxategi, E., Arnaiz, A., Aranzabe, E., Aranzabe, A., Villar, A.: On line sensors for condition monitoring of lubricating machinery. In: Proceedings of 22nd International Congress on Condition Monitoring and Diagnostic Engineering Management COMADEM, San Sebastian, Spain (2009)
Mang, T., Dresel, W.: Lubricants and lubrication. WILEY-VCH Verlag GmbH, Weinheim, Germany (2007)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Elomaa, T., Kääriäinen, M.: An analysis of reduced error pruning. Journal of Artificial Intelligence Research 15, 163–187 (2001)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)
Breiman, L.: Using iterated bagging to debias regressions. Machine Learning 45(3), 261–277 (2001)
Drucker, H.: Improving regressors using boosting techniques. In: ICML 1997: Proceedings of the Fourteenth International Conference on Machine Learning, pp. 107–115. Morgan Kaufmann Publishers Inc., San Francisco (1997)
Rodríguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(10), 1619–1630 (2006)
Zhang, C., Zhang, J., Wang, G.: An empirical study of using rotation forest to improve regressors. Applied Mathematics and Computation 195(2), 618–629 (2008)
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Bustillo, A., Villar, A., Gorritxategi, E., Ferreiro, S., Rodríguez, J.J. (2011). Using Ensembles of Regression Trees to Monitor Lubricating Oil Quality. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6703. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21822-4_21
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