A DECISION TREE-BASED CLASSIFICATION FRAMEWORK FOR USED
OIL ANALYSIS APPLYING RANDOM FOREST FEATURE SELECTION
|Volume||JASETD Volume 2 Issue 1 2018|
|Authors||WAKIRU*1, J., PINTELON1, L., CHEMWENO1, P., & MUCHIRI2, P.N.|
|Article Type||Research article|
|First Published||April 2018|
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.