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Rolling bearing fault feature extraction method based on energy slice wavelet transformation

An energy slicing and wavelet transform technology, which is used in the testing of mechanical bearings, measuring devices, and mechanical components, and can solve the problems of reducing the frequency resolution of FSWT analysis and insufficient noise immunity of FSWT.

Active Publication Date: 2016-10-26
SHIJIAZHUANG TIEDAO UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Vibration signals collected on site are often mixed with strong narrow-band pulses and random noise interference. Research has found that the noise mixed into the signal will reduce the frequency resolution of FSWT analysis. Therefore, FSWT has serious shortcomings in terms of noise immunity.

Method used

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  • Rolling bearing fault feature extraction method based on energy slice wavelet transformation
  • Rolling bearing fault feature extraction method based on energy slice wavelet transformation
  • Rolling bearing fault feature extraction method based on energy slice wavelet transformation

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Embodiment Construction

[0034] The technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0035] In the following description, a lot of specific details are set forth in order to fully understand the present invention, but the present invention can also be implemented in other ways different from those described here, and those skilled in the art can do it without departing from the meaning of the present invention. By analogy, the present invention is therefore not limited to the specific examples disclosed below.

[0036] Aiming at the sh...

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Abstract

The invention discloses a rolling bearing fault feature extraction method based on energy slice wavelet transformation, and relates to the technical field of bearing fault diagnosis methods. The method comprises the following steps: firstly introducing an energy slice to wavelet transformation; secondly obtaining the time-frequency distribution of a vibrating signal in a full frequency band through employing wavelet transformation, selecting a time-frequency target region according to the obtained energy distribution characteristics of the vibrating signal, and segmenting a time-frequency region comprising the fault features; finally carrying out the reconstruction of a signal component of the target region through inverse transformation, and separating effective signal time-frequency features. The simulation data and the fault diagnosis result of the data of a rolling bearing indicate that the method can effectively extract the fault characteristic frequency information of the rolling bearing, and verifies the effectiveness.

Description

technical field [0001] The invention relates to the technical field of a fixed diagnosis method for bearings, in particular to a rolling bearing fault feature extraction method based on energy slice wavelet transform. Background technique [0002] Rolling bearings are the most widely used key components in rotating machinery, and their working status directly affects the operating efficiency and service life of the rotating machinery system. However, due to the internal excitation mechanism of the bearing, complex background noise and other interference sources, the fault feature information is weak and usually appears in the form of modulation, and it is difficult to extract fault features. [0003] The time-frequency analysis method can simultaneously extract the local information in the time domain and frequency domain of the signal, and has been widely used in the vibration fault diagnosis of rotating machinery. Typical time-frequency analysis methods include short-time...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/04
CPCG01M13/045
Inventor 马增强李亚超杨绍普刘永强王建东张俊甲王梦奇王永胜宋子彬张安
Owner SHIJIAZHUANG TIEDAO UNIV