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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.

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.

Big data and related technologies are expected to drastically change the way industrial maintenance is managed. However, at the moment, many companies are collecting large amounts of data without knowing how to systematically exploit it. It is therefore important to find new ways of evaluating and quantifying the value of data. This paper addresses the value of data-based profitability of maintenance investments.

An analytical wasted value of data model (WVD-model) is presented to quantify how the value of data can be increased through eliminating waste. The use of the model is demonstrated with a case example of a maintenance investment appraisal of an automotive parts manufacturer.

The presented model contributes to the gap between the academic research and the solutions implemented in practice in the area of value optimization. The model provides a systematic way of evaluating if the benefits of investing in maintenance data exceed the additional costs incurred. Applying the model to a case study revealed that even though the case company would need to spend more time in analyzing and processing the increased data, the investment would be profitable if even a modest share of the current asset failures could be prevented through improved data analysis.

The model is designed and developed on the principle of eliminating waste to increase value, which has not been previously extensively discussed in the context of data management.