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A data mining approach for lubricant-based fault diagnosis

James Wakiru (Centre for Industrial Management/Traffic and Infrastructure, KU Leuven, Leuven, Belgium)
Liliane Pintelon (Centre for Industrial Management/Traffic and Infrastructure, KU Leuven, Leuven, Belgium)
Peter Muchiri (Dedan Kimathi University of Technology, School of Engineering, Nyeri, Kenya)
Peter Chemweno (Department of Design, Production and Management, University of Twente, Enschede, The Netherlands)

Journal of Quality in Maintenance Engineering

ISSN: 1355-2511

Article publication date: 9 July 2020

Issue publication date: 27 April 2021




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.


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.


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.


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.



Wakiru, J., Pintelon, L., Muchiri, P. and Chemweno, P. (2021), "A data mining approach for lubricant-based fault diagnosis", Journal of Quality in Maintenance Engineering, Vol. 27 No. 2, pp. 264-291.



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Copyright © 2020, Emerald Publishing Limited

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