A decision tree-based classification framework for used oil analysis applying random forest feature selection

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dc.contributor.author Wakiru, James Mutuota
dc.contributor.author L., Pintelon
dc.contributor.author P., CHEMWENO
dc.contributor.author Muchiri, Peter Ng’ang’a
dc.date.accessioned 2018-05-29T07:07:50Z
dc.date.available 2018-05-29T07:07:50Z
dc.date.issued 2018-05
dc.identifier.issn 2309-0936
dc.description.abstract Lubricant condition monitoring (LCM), part of condition monitoring techniques under Condition Based Maintenance, monitors the condition and state of the lubricant which reveal the condition and state of the equipment. LCM has proved and evidenced to represent a key concept driving maintenance decision making involving sizeable number of parameter (variables) tests requiring classification and interpretation based on the lubricant’s condition. Reduction of the variables to a manageable and admissible level and utilization for prediction is key to ensuring optimization of equipment performance and lubricant condition. This study advances a methodology on feature selection and predictive modelling of in-service oil analysis data to assist in maintenance decision making of critical equipment. Proposed methodology includes data pre-processing involving cleaning, expert assessment and standardization due to the different measurement scales. Limits provided by the Original Equipment Manufacturers (OEM) are used by the analysts to manually classify and indicate samples with significant lubricant deterioration. In the last part of the methodology, Random Forest (RF) is used as a feature selection tool and a Decision Tree-based (DT) classification of the in-service oil samples. A case study of a thermal power plant is advanced, to which the framework is applied. The selection of admissible variables using Random Forest exposes critical used oil analysis (UOA) variables indicative of lubricant/machine degradation, while DT model, besides predicting the classification of samples, offers visual interpretability of parametric impact to the classification outcome. The model evaluation returned acceptable predictive, while the framework renders speedy classification with insights for maintenance decision making, thus ensuring timely interventions. Moreover, the framework highlights critical and relevant oil analysis parameters that are indicative of lubricant degradation; hence, by addressing such critical parameters, organizations can better enhance the reliability of their critical operable equipment. en_US
dc.language.iso en en_US
dc.publisher Journal of Applied Sciences, Engineering and Technology for Development JASETD en_US
dc.relation.ispartofseries Volume 3;Issue 1
dc.subject Random forest, Decision trees, oil analysis, Maintenance decision support, Lubricant condition monitoring en_US
dc.title A decision tree-based classification framework for used oil analysis applying random forest feature selection en_US
dc.type Article en_US

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