Using ensembles of regression trees to monitor lubricating oil quality

Authors:
Andres Bustillo;Alberto Villar;Eneko Gorritxategi;Susana Ferreiro;Juan J. Rodríguez
Affiliations:
University of Burgos, Spain;Fundación TEKNIKER, Eibar, Guipúzcoa, Spain;Fundación TEKNIKER, Eibar, Guipúzcoa, Spain;Fundación TEKNIKER, Eibar, Guipúzcoa, Spain;University of Burgos, Spain
Venue:
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Year:
2011

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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.