Predictive algorithm to determine the suitable time to change automotive engine oil

Recently, emerging technologies related to various sensors, product identification, and wireless communication give us new opportunities for improving the efficiency of automotive maintenance operations, in particular, implementing predictive maintenance. The key point of predictive maintenance is to develop an algorithm that can analyze degradation status of automotive engine oil and make predictive maintenance decisions. In this study, as a basis for implementing the predictive maintenance of automotive engine oil, we propose an algorithm to determine the suitable change time of automotive engine oil by analyzing its degradation status with mission profile data. For this, we use several statistical methods such as factor analysis, discriminant and classification analysis, and regression analysis. We identify main factors of mission profile and engine oil quality with factor analysis. Subsequently, with regression analysis, we specify relations between main factors considering the types of mission profile of automotive engine oil: urban-mode and highway-mode. Based on them, we determine the proper change time of engine oil through discriminant and classification analysis. To evaluate the proposed approach, we carry out a case study and have discussion about limitations of our approach.