Abstract:
Purpose – The purpose of this paper is to develop a maintenance decision support system (DSS) framework
using in-service lubricant data for fault diagnosis. The DSS reveals embedded patterns in the data (knowledge
discovery) and automatically quantifies the influence of lubricant parameters on the unhealthy state of the
machine using alternative classifiers. The classifiers are compared for robustness from which decision-makers
select an appropriate classifier given a specific lubricant data set.
Design/methodology/approach – The DSS embeds a framework integrating cluster and principal
component analysis, for feature extraction, and eight classifiers among them extreme gradient boosting (XGB),
random forest (RF), decision trees (DT) and logistic regression (LR). A qualitative and quantitative criterion is
developed in conjunction with practitioners for comparing the classifier models.
Findings – The results show the importance of embedded knowledge, explored via a knowledge discovery
approach. Moreover, the efficacy of the embedded knowledge on maintenance DSS is emphasized. Importantly,
the proposed framework is demonstrated as plausible for decision support due to its high accuracy and
consideration of practitioners needs.
Practical implications – The proposed framework will potentially assist maintenance managers in
accurately exploiting lubricant data for maintenance DSS, while offering insights with reduced time and errors.
Originality/value – Advances in lubricant-based intelligent approach for fault diagnosis is seldom utilized in
practice, however, may be incorporated in the information management systems offering high predictive
accuracy. The classification models’ comparison approach, will inevitably assist the industry in selecting
amongst divergent models’ for DSS
