Mathematical Model for a Learning System in Early Diagnostics of the Electromechanical Drive Gearbox

Abstract

Currently, active design and development of an "electric aircraft" are underway that involves introducing the electromechanical steering drives in the control system instead of the traditionally used hydraulic drives. In order to ensure and improve the flight safety indicators when using such drives, it is necessary to develop a system that provides early diagnostics of the technical condition of such drives. Modern methods in the intelligent big data analysis could be used to diagnose and forecast the residual life depending on the actual operating conditions and signs of degradation. Creation of algorithms based on such methods requires formalization of the diagnostic task and availability of the arrays of data on the drive functioning in various states. For this purpose, a mathematical model is developed; it is confirmed by the experimental data describing the typical structure and working processes in the drive during degradations associated with alterations in friction and backlash. The paper identifies direct and hidden signs of the degradation; based on results of analyzing the dynamic characteristics, it determines their significance for classification by the measured diagnostic signals. By establishing the boundaries, within which the degradation signs could be found, the drive aggregated technical states are identified making it possible to create a system for the gearbox state classification. Large-scale simulation was carried out with varying the significant degradation signs and environmental parameters, which results would be used to form samples containing valuable information on the failure development, and to develop algorithms ensuring an efficient solution to the problems of classification and forecasting the drive technical state

The work was partially supported by the Russian Science Foundation (project no. 23-19-00464)

Please cite this article in English as:

Skryabin A.V. Mathematical model for a learning system in early diagnostics of the electromechanical drive gearbox. Herald of the Bauman Moscow State Technical University, Series Mechanical Engineering, 2025, no. 3 (154), pp. 71--92 (in Russ.). EDN: QHNESI

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