Bearing fault diagnosis and prediction method based on extended Kalman filtering algorithm

Abstract

The invention discloses a bearing fault diagnosis and prediction method based on an extended Kalman filtering algorithm, and the method comprises the following steps: 1) employing a full service life cycle vibration signal of a bearing; 2) constructing an AR model through the vibration signal, carrying out the filtering analysis of the vibration signal, and highlighting a signal correlated with a fault; 3) extracting energy information correlated with a wavelet packet coefficient through employing wavelet packet transformation, and constructing a feature character; 4) carrying out the calculation of a mahalanobis distance, constructing health indexes based on the mahalanobis distance, converting the non-negative and non-Gaussian distribution health indexes into Gaussian distribution data through Box-Cox transformation, and determining a related abnormal threshold range through the features of Gaussian distribution and the inverted Box-Cox transformation; 5) carrying out fitting analysis of health index data in a loss period, constructing a degeneration model and a status space model, updating model parameters through employing current data and the extended Kalman filtering algorithm, and predicting the remaining service life of the bearing. The method is higher in prediction precision, and is shorter in consumed time.

Description

technical field [0001] The invention belongs to the field of bearing fault diagnosis and prediction, and in particular relates to a bearing fault diagnosis and prediction method based on an extended Kalman filter algorithm. Background technique [0002] Bearings are an indispensable part of rotating machinery. They are widely used in electric power, petrochemical, metallurgy, machinery, aerospace, and some military industrial sectors. They are used to ensure the accuracy, performance, and The core component of life and reliability, but also one of the most vulnerable parts. According to statistics, many failures of rotating machinery are caused by bearing damage. If the bearing fails, some functions of the system will be reduced or lost, and serious or even catastrophic consequences will be caused. Therefore, bearing condition monitoring, fault diagnosis and fault prediction have been the research focus in recent years. Vibration signals are widely used in condition monit...

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the shortcomings of low prediction accuracy and long time-consuming of existing bearing fault diagnosis and prediction methods, the present

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