Lubrication Condition monitoring (LCM) is not only utilized as an early warning system in machinery but also, for fault diagnosis and prognosis under condition-based maintenance (CBM). LCM is considered as an important condition monitoring technique, due to the ample information derived from lubricant testing, which demonstrates an introspective reflection on the condition and state of the machinery and the lubricant. Central to the entire LCM program is the application concept, where information from lubricant analysis is evaluated (for knowledge extraction) and analyzed with a view of generating an output which is interpretable and applicable for maintenance decision support (knowledge application). For robust LCM, varying techniques and approaches are used for extracting, processing and analyzing information for decision support. For this reason, a comprehensive overview of applicative approaches for LCM is necessary, which would aid practitioners to address gaps as far as LCM is concerned in the context of maintenance decision support. However, such an overview, is to the best of our knowledge, lacking in the literature, hence the objective of this review article. This paper systematically reviews recent research trends and development of LCM based approaches applied for maintenance decision support, and specifically, applications in equipment diagnosis and prognosis. To contextualize this concern, an initial review of base oils, additives, sampling and testing as applied for LCM and maintenance decision support is discussed. Moreover, LCM tests and parameters are reviewed and classified under varying categories which include, physiochemical, elemental, contamination and additive analysis. Approaches applicable for analyzing data derived from LCM, here, lubricant analysis for maintenance decision support are also classified into four categories: statistical, model-based, artificial intelligence and hybrid approaches. Possible improvement to enhance the reliability of the judgement derived from the approaches towards maintenance decision support are further discussed. This paper concludes with a brief discussion of plausible future trends of LCM in the context of maintenance decision making. This present study, not only highlights gaps in existing literature, by reviewing approaches applicable for extracting knowledge from LCM data for maintenance decision support, it also reviews the functional and technical aspects of lubrication. This is expected to address gaps in both theory and practice as far as LCM and maintenance decision support are concerned.
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