INTELLIGENT METHODS FOR CONDITION MONITORING AND LIFETIME PREDICTION OF GEARBOXES

Abstract

The article presents an analysis of modern methods for monitoring the condition and predicting the service life of gearboxes, which are key components of industrial systems. Traditional diagnostic approaches are considered, including vibration analysis, acoustic diagnostics, and thermal monitoring, which, despite their wide application, have limited informativeness and do not fully allow for accurate prediction of the re-maining useful life of equipment. Particular attention is given to modern methods based on artificial intelli-gence and machine learning, which enable the automatic extraction of signal parameters (vibroacoustic, thermal, etc.) that carry information about the equipment’s condition, reveal nonlinear patterns, and allow the construction of reliable models of equipment failure processes. Three main directions of intelligent diagnostics are analyzed: machine learning methods, neural network approaches, and hybrid systems that combine signal preprocessing algorithms with deep learning models.