A predictive algorithm for estimating the quality of vehicle engine oil

Recently, with emerging technologies, visibility of vehicle information over the whole lifecycle becomes possible. The visibility opens up new challenging issues for improving the efficiency of vehicle operations. One of the most challenging problems arising during the middle of life (MOL) of vehicles is the predictive maintenance on engine oil. For this, in this study, we focus on developing a predictive algorithm to estimate the quality of the engine oil of a vehicle by analyzing its degradation status with mission profile data. For this purpose, we specify the relations between indicators of engine mission profiles and oil quality indicators using principal component analysis and regression method. Then, we develop a heuristic algorithm for estimating the value of a quality indicator of engine oil based on them. To evaluate the proposed approach, we carry out a case study and computational experiments. Significance: This study deals with a prognostic maintenance approach for vehicle engine oil, which recently has been highlighted as a cost-effective method using product status data provided by emerging technologies.

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