A supervised machine learning model for determining lubricant oil operating conditions

Abstract

AbstractMachine learning tools for analysing lubricating oil data have enabled better information in the condition based maintenance (CBM) approach to the process of diagnosing and predicting failures in diesel‐powered vehicle fleets. With the increase in the number of sensors inserted in vehicles, it is possible for companies to stockpile large quantities of information in real time. As such, the development of data‐driven tools will enable accurate identification of the level of wear of a system, evolving CBM into a more reliable and dynamic approach. Following this type of data‐based analysis focusing on determining the wear of systems and equipment, this paper presents an intelligent system for assessing the condition of lubricating oil in automotive diesel engines. To this end, we analyse the use of raw data obtained from the sensors installed in the car and evaluate in conjunction with the insertion of engineered features designed the best way to determine the operating state of the oils. The results presented in this analysis show that to explain 90% of the variation in the original data only the variables kinematic viscosity, dynamic viscosity, engine oil temperature and OSF_v3 are needed. After evaluating the quality of the variables, we conducted an experimental study to analyse the performance of various machine learning algorithms, taking into account the number of features as input data. The results show that the proposed system has the ability to identify the operating conditions of lubricating oil using seven variables as input to a model based on gradient boosting, obtaining a recall result of 93%, precision of 96% and F1‐score of 94%. We conducted a set of additional studies to understand how different subsets of variables affected the performance of the models, and the results show that the best combination includes information regarding the engine speed, coolant and oil temperature, oil pressure, the oil stress factor (OSF_v3), kinematic viscosity, and the dynamic viscosity.