Rolling bearing fault diagnosis method based on conditional empirical wavelet transformation

An empirical wavelet, rolling bearing technology, applied in the testing of mechanical components, testing of machine/structural components, measuring devices, etc., can solve problems such as inappropriate frequency spectrum division, pre-defined modal number, etc.

Active Publication Date: 2019-01-15
SICHUAN UNIV
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a rolling bearing fault diagnosis method based on conditional empirical wavelet transform, which solves the problems of pre-definitio

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  • Rolling bearing fault diagnosis method based on conditional empirical wavelet transformation
  • Rolling bearing fault diagnosis method based on conditional empirical wavelet transformation
  • Rolling bearing fault diagnosis method based on conditional empirical wavelet transformation

Examples

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[0122] Example 1: Modal selection

[0123] In order to demonstrate the advantages of the MRCC indicator in modal selection, an example is used to compare the signal kurtosis value K in time domain t , The frequency band spectrum kurtosis value K used by Fast Kurtogram b The performance of, RCC and MRCC in modal selection. If these indicators of a certain mode are the largest, it means that these indicators select that mode. The time-domain diagram of an inner ring fault vibration signal decomposed into three modes by EWT is as follows: Figure 3a-Figure 3c As shown, the squared envelope spectrum decomposed into three modes by EWT is as follows Figure 4a-Figure 4c Shown. It can be seen from the figure that although from the time-domain form, mode 1 and mode 2 have more impacts, the inner ring fault characteristic frequency f is not found in the square envelope spectrum. i . This shows that the shock in mode 1 and mode 2 is not caused by the inner ring failure. On the contrary, ...

Example Embodiment

[0127] Example 2: Fault diagnosis of rolling bearing

[0128] In order to demonstrate the effect of the present invention of "fault diagnosis of rolling bearing based on conditional experience wavelet and improved cyclic component ratio", this example takes the disclosed bearing outer ring fault data as an example, and uses the present invention, EWT and Fast kurtogram to process the data respectively. In order to make the comparison fair, pre-whitening processing is used as the pre-processing means of the three methods, and the pre-whitening processing method used in Fast Kurtogram is used. In addition, the number of EWT modes remains the same as the number of modes of the present invention, and the number of modes of the present invention is automatically determined by the MRCC. The bearing data uses the high-speed bearing outer ring fault data provided by Acoustics and VibrationDatabase, and the bearing vibration data is collected by GarethForbes of Curtin University. The sam...

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Abstract

The invention relates to the field of bearing fault diagnosis, discloses a rolling bearing fault diagnosis method based on conditional empirical wavelet transformation, and aims to improve the accuracy of fault feature extraction and fault type identification of a bearing. The method is characterized by comprising the following steps of firstly, acquiring a bearing vibration signal; secondly, setting n frequency spectrum maximum values, and carrying out conditional empirical wavelet transformation on the bearing vibration signal on the basis of the n frequency spectrum maximum values; thirdly,calculating cyclic component ratios of modes after transformation, and storing the maximum values and the modes corresponding to the maximum values; fourthly, if the currently stored maximum cyclic component ratio is larger than a reference cyclic component ratio, defining that n=n+1, taking the currently stored maximum cyclic component ratio as a new reference cyclic component ratio, and repeating the operation; fifthly, selecting out the mode corresponding to the maximum cyclic component ratio stored each time again; and finally, calculating a square envelope spectrum of the selected mode.The method is suitable for the rolling bearing fault diagnosis.

Description

technical field [0001] The invention relates to the field of bearing fault diagnosis, in particular to a rolling bearing fault diagnosis method based on conditional experience wavelet transform. Background technique [0002] Rolling bearings are important components in rotating machinery and play a role in supporting the rotation of rotating parts. When a fault occurs on the bearing, it will not only generate vibration and noise, but also affect the running accuracy of the rotating parts; if the fault cannot be detected in time, it may cause large safety accidents and economic losses. Therefore, early detection of bearing failure is particularly important. According to the structure of the bearing and the location of the fault point, the characteristic frequencies generated when the inner ring, outer ring and rolling element fail can be calculated, which are called the inner ring fault characteristic frequency, outer ring fault characteristic frequency and rolling element f...

Claims

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Application Information

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IPC IPC(8): G01M13/045
CPCG01M13/045
Inventor 苗强莫贞凌王剑宇张恒刘慧宇曾小飞
Owner SICHUAN UNIV
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