Prerequisites for Using a Digital Twin to Determine the Limiting State of Engine Oil or Predict Its Remaining Life

Disadvantages associated with the frequency of engine oil changes exist in the currently used maintenance system for vehicles and agricultural mechanization. The actual condition of the engine oil may differ from the desired condition when changing. The purpose of this study was to determine the prospects for creating a digital twin for predicting the remaining life of motor oil. Methods for monitoring the current state of engine oil are analyzed in the work. A reliable method of continuous monitoring has not yet been found. It is shown that the main factors that should be taken into account when predicting the service life of motor oil are the load and speed conditions of the engine. The main parameters characterizing the serviceability of motor oil are base number and kinematic viscosity. Statistically based mathematical models reflecting the “aging” process of motor oil were selected based on known data. Modeling of changes in kinematic viscosity shows a fairly high probability of error and does not reflect an unambiguous pattern. It has been established that changes in the alkaline number of motor oil can occur according to exponential laws. The selected models of changes in the base number describe the experimental data quite accurately. The coefficient of determination ranged from 0.965 to 0.999. The conducted studies indicate the possibility of using a digital twin to predict the remaining oil life. But the data found in open sources provides a fundamental understanding of the patterns, but is quite fragmented: they relate to different cars, tractors and types of motor oils. Therefore, to create a digital twin, comprehensive experimental studies are required to assess the degradation of motor oil at different speed and load conditions.