Forecasting the resource of machine parts based on the dynamics of changes in the wear mechanism using the neural network method - Aulin, V.V.; Chumak, V.M.; Lysenko, S.V. (Problems of Tribology, 2026)
The problem of predicting the residual resource of tribocouplings of resource-determining parts of automotive and agricultural machinery under conditions of operational change of the dominant wear mechanism is considered. It is shown that in real operating conditions the wear mechanism does not remain static: degradation of lubricants, change of load-speed regimes and variability of soil conditions cause a transition from a regular mechanism to an emergency one, which is accompanied by a sharp reduction in the residual resource of machine parts. A two-stage system for identifying the change in the dominant wear mechanism is proposed, combining a classifier based on long short-term memory with an entropy transition detector, which develops an entropy approach to the analysis of tribosystems. The data set is formed on the basis of a modified Archard model with five calibrated coefficients for different lubrication modes and a Palmgren-Miner model in the form of an endurance curve with realistic values of the basic number of cycles 108 ...109 for different materials of tribocoupling parts. Validation on eight scenarios of transition between wear mechanisms demonstrated reliable detection of the change in the dominant mechanism, the warning time for reaching a critical state of 229…593 hours for automotive parts and 13…21 hours for agricultural parts, which is 5.6…7.1% of the total resource and is sufficient for planning maintenance of machines