ПРОГНОЗНЫЙ МОНИТОРИНГ ТЕХНИЧЕСКОГО СОСТОЯНИЯ ПУТЕМ АНАЛИЗА СОСТАВЛЯЮЩИХ СИГНАЛА ВИБРАЦИИ ПОДШИПНИКОВ ВРАЩАЮЩИХСЯ ЭЛЕКТРИЧЕСКИХ МАШИН

Abstract

Relevance In industry, any failures and unplanned downtime of machines or systems can degrade or interrupt a company's core business, potentially leading to significant penalties and irreparable loss of reputation. Existing traditional maintenance approaches (failure or routine maintenance) suffer from some assumptions and limitations, such as high prevention or repair costs and inadequate or inaccurate mathematical degradation processes. Due to the trend of intelligent manufacturing, data mining, and artificial intelligence, predictive maintenance control is proposed as a new type of maintenance paradigm only after analytical models predict certain failures or degradations. Equipment condition monitoring can reduce the likelihood of failures occurring. Successful technical diagnostics and monitoring of complex electromechanical subsystems and assemblies provides an increase in service life and reliability of the systems as a whole. Aim of research A general overview of maintenance aims and objectives, which mainly include detection and prevention of failures and malfunctions, maintaining performance within established limits, condition prediction in order to improve machine reliability and full use of its resource, cost minimization, maximization of availability/reliability and multi-criteria optimization. Object of research The object of the study is the technical condition of rolling bearings of rotating electric machines. The main defects that are characteristic of this object are analyzed. In addition, a practical and effective sequential preventive maintenance method for rotating electrical machines is proposed, consisting of four steps: feature generation and fault diagnosis selection, data dimensionality reduction and feature fusion, fault detection and diagnosis, and estimation of the remaining useful life of rotating electrical machine bearings. Vibration signal analysis (from the time domain waveform and spectrum of the original or derivative signal, characteristics can be calculated and their effectiveness in diagnosing a fault can be assessed). Research methods To verify the performance of the proposed method, bearing failure is simulated in the Simulink MATLAB module. The bearing failure is created by feeding the vibration perturbation into the shaft system. Results Based on the results of the simulation, a conclusion on the operability of the proposed method for investigating the technical condition of rotating electric machines is drawn.