Rolling bearing residual life prediction method based on hybrid filtering and state monitoring

Abstract

The invention discloses a method for predicting the residual life of a rolling bearing based on hybrid filtering and state monitoring. The method comprises the following steps: firstly, acquiring a horizontal vibration signal in the running process of the rolling bearing; then calculating kurtosis and a root mean square RMS value by using the horizontal vibration signal, respectively determining the kurtosis and the RMS value of the horizontal vibration signal as a state monitoring index and a prediction index, and then monitoring the running state of the bearing by using a Kalman filtering algorithm KF and determining a fault starting point FST; after the bearing enters a degeneration state, estimating a future root-mean-square value by adopting a hybrid filtering algorithm combining particle filtering (PF) and unscented Kalman filtering (UKF); and finally, establishing a sliding window and a linear model on the obtained root-mean-square estimated value to determine a failure threshold value of the bearing, and judging the moment when the root-mean-square value exceeds the failure threshold value to obtain a residual life prediction result. According to the method, the fault starting point is determined by monitoring the running state of the rolling bearing, and an accurate residual life prediction result is obtained.

Description

technical field [0001] The invention belongs to the technical field of life prediction of rotating machinery, and in particular relates to a method for predicting the remaining life of a rolling bearing based on hybrid filtering and state monitoring. Background technique [0002] Rolling bearings are the key components of rotating machinery. Their operating status directly determines the performance of the machinery. Once a failure occurs, it will cause major safety accidents. Therefore, it is necessary to monitor the operating status of rolling bearings in real time and analyze the performance degradation process. Study remaining service life prediction methods to ensure safe operation of machinery. [0003] Aiming at the problem that it is difficult to determine the fault starting point and failure threshold in the residual life prediction of rolling bearings, a residual life prediction method based on hybrid filtering and condition monitoring is proposed. This method can ...

AI Technical Summary

Problems solved by technology

[0003] Aiming at the problem that the fault initiation point and failure threshold are difficult to determine in the remaining life prediction of rolling bearings, a method for residual life prediction based on hybrid filtering and condition monitoring is proposed, which can effectively identify the fault initiation point and failure threshold, and Accurately predict the remaining life, with high prediction accuracy, and provide a further reference for the prediction of the remaining life of rolling bearings

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