Aerospace Instrument-Making Annotation << Back Methods and Models for Diagnostics of Electromechanical Components of Aerial Vehicle using Machine Learning Veresnikov G.S. Due to the development of big data mining technologies and the need to solve the urgent problem of improving the safety of aviation transport systems, a promising area of scientific research is the use of machine learning methods to assess the technical condition of aerial vehicle components and assemblies. The properties of these methods, which make it possible to fi nd diffi cult-to-formalize patterns in empirical data and form conclusions, are proposed to be used to create learning systems for early diagnosis of aircraft that increase their characteristics as valuable practical experience is accumulated. The paper analyzes the approaches and methods that can be used for monitoring of electromechanical components, which are widely used in the creation of aerial vehicle characterized by a high degree of electrification. Based on this analysis, integration models have been developed that refl ect grouping, hierarchy, generalized relationships, and the sequence of application of data processing methods in early diagnosis systems based on machine learning. The developed models have been tested in the synthesis of algorithms for assessing the technical condition of the electromechanical actuator of an unmanned aerial vehicle of an airplane type. Keywords: diagnostics, electromechanical components, feature extraction, machine learning, classification, forecasting. DOI: 10.25791/aviakosmos.5.2025.1481 Pp. 31-39.