Unscented Kalman filter algorithm-based method for predicting residual life of bearing

Abstract

The invention discloses an unscented Kalman filter algorithm-based method for predicting the residual life of a bearing. The method comprises the following steps of 1) acquiring a full-life periodic vibration signal of the bearing; 2) calculating an effective value by utilizing the vibration signal and constructing an index that reflects a health state of the bearing based on the effective value; 3) calculating a health index change quantity at each moment and intercepting health index data in a bearing wear period by utilizing a rectangular window function and a K-S test; and 4) performing fitting analysis on the health index data in the wear period, constructing a state space model that represents a bearing degradation process, updating model parameters by utilizing the health index data obtained by observation currently and an unscented Kalman filter algorithm, and predicting the residual life of the bearing. The unscented Kalman filter algorithm-based method for predicting the residual life of the bearing, provided by the invention, is relatively high in prediction precision.

Description

technical field [0001] The invention belongs to the field of bearing fault diagnosis and prediction, in particular to a bearing remaining life prediction method based on an unscented Kalman filter algorithm. Background technique [0002] As a key component, bearings are widely used in electric power, petrochemical, metallurgy, machinery, aerospace and other rotating machinery, but unfortunately, it is also a frequent component of failure in rotating machinery. According to statistics, the service life of main bearings of aero-engines is only hundreds of hours, and the precision life of high-speed bearings of CNC machine tools is thousands of hours. normal work. Therefore, bearing condition monitoring, fault diagnosis and fault prediction have been the research focus in recent years. Considering that the bearing is a nonlinear process from early failure to development until failure, the nonlinear filtering algorithm using Bayesian theory, such as extended Kalman filter, par...

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the shortcomings of the existing nonlinear Bayesian filter algorithm in solving the problem of low remaining life prediction accuracy in bearing fault prediction, the present invention provides a bearing remaining life prediction based on the unscented Kalman filter algorithm with high prediction accuracy method

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