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

An extended Kalman filter algorithm technology, which is applied in the field of bearing fault diagnosis and prediction based on the extended Kalman filter algorithm, can solve the problems of short time consumption, long time consumption, and low prediction accuracy

Active Publication Date: 2016-02-03
吴江市民福电缆附件厂
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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 invention provides a bearing fault diagnosis based on extended Kalman filter algorithm with high prediction accuracy and short time consumption and forecasting methods

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

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Embodiment Construction

[0057] The present invention will be further described below in conjunction with the accompanying drawings.

[0058] refer to Figure 1 to Figure 9 , a bearing fault diagnosis and prediction method based on an extended Kalman filter algorithm, the method includes the following steps:

[0059] S1. Collect the vibration signal of the whole life cycle of the bearing;

[0060] S2. Use the vibration signal when the bearing is healthy to construct an AR model, use the AR model to filter the collected vibration signal, and highlight the information related to the vibration signal and the fault, so as to facilitate the construction of the subsequent health index, fault diagnosis and prediction;

[0061] S3. Use wavelet packet transform to analyze the residual signal after AR model filtering, and construct the energy feature corresponding to the wavelet packet coefficient for Mahalanobis distance calculation;

[0062] S4. Carry out the calculation of the Mahalanobis distance, and con...

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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...

Claims

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Application Information

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IPC IPC(8): G01M13/04G06F19/00
Inventor 金晓航阙子俊孙毅单继宏
Owner 吴江市民福电缆附件厂
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