Classification of Signal Types and Machining Methods for Intelligent Assessment of the Technical Mill of Mobile Machines for Agro-Industrial Production

The article provides a critical review of current research on the use of machine learning (ML) methods for identifying the technical state of units and mechanisms of agro-industrial production machines for different types of diagnostic signals (vibration, acoustic, temperature, vice, etc.). The exchange of threshold diagnostic strategies is shown and the need for an intelligent technical service system is highlighted, which integrates a sensor base and adaptive MN algorithms for online assessment of the plant, predictive maintenance and minimization of untransferable downtime. The concept of complex signal classification and selection of algorithms is proposed and creates a framework for scaling solutions adapted to specific agro-industrial production machines calls.