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Rolling bearing fault diagnosis method based on multi-scale dispersion entropy and VPMCD

A rolling bearing and fault diagnosis technology, which is applied in character and pattern recognition, mechanical component testing, machine/structural component testing, etc. Robust and efficient effects

Pending Publication Date: 2021-04-20
KUNMING UNIV OF SCI & TECH
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  • Summary
  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0004] The invention provides a rolling bearing fault diagnosis method based on multi-scale dispersive entropy and VPMCD, which solves the problems of difficult extraction of rolling bearing fault features and low recognition accuracy

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  • Rolling bearing fault diagnosis method based on multi-scale dispersion entropy and VPMCD
  • Rolling bearing fault diagnosis method based on multi-scale dispersion entropy and VPMCD
  • Rolling bearing fault diagnosis method based on multi-scale dispersion entropy and VPMCD

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Embodiment 1

[0054] Embodiment 1: as Figure 1-5As shown, the rolling bearing fault diagnosis method based on multi-scale dispersive entropy and VPMCD, firstly, the maximum correlation kurtosis deconvolution is used to denoise the collected original vibration signal of the bearing, which is used to enhance the fault characteristics of the signal; secondly, using The variational mode decomposition method decomposes the noise-reduced signal to obtain a series of eigenmode functions; again, calculate the multi-scale distribution entropy value of each eigenmode function to form the fault feature vector; finally, adopt A trained variable predictive model classifier (VPMCD classifier) ​​is used for fault identification and classification.

[0055] As a further solution of the present invention, the specific steps of the method are as follows:

[0056] Step 1. Collect the vibration signal of the rolling bearing through the acceleration sensor above the bearing seat at the drive end of the motor....

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Abstract

The invention relates to a rolling bearing fault diagnosis method based on multi-scale dispersion entropy and VPMCD and belongs to the technical field of mechanical equipment fault diagnosis. The method comprises the following steps of firstly, carrying out noise reduction processing on an acquired original vibration signal of a bearing by adopting maximum correlation kurtosis deconvolution to enhance fault characteristics of the signal; secondly, decomposing the denoised signal by using a variational mode decomposition method to obtain a series of intrinsic mode functions; thirdly, calculating a multi-scale dispersion entropy value of each intrinsic mode function to form a fault feature vector; and finally, carrying out fault identification and classification by adopting a trained variable prediction model classifier. The method can effectively solve problems that fault features are difficult to extract and the recognition precision is low, and improves accuracy of fault recognition.

Description

technical field [0001] The invention relates to a rolling bearing fault diagnosis method based on multi-scale dispersion entropy (Multi-scale Dispersion Entropy, MDE) and VPMCD, and belongs to the technical field of mechanical equipment fault diagnosis. Background technique [0002] Rotating mechanical equipment is widely used in aerospace, machinery manufacturing, high-speed trains and other fields. Rolling bearings are indispensable as one of its components, so they play an important role in rotating machinery. However, because rolling bearings are prone to fault diagnosis under harsh environment and high-intensity work, it is worthwhile to use effective methods to extract fault features and perform fault diagnosis. [0003] The vibration signal collected by the acceleration sensor is easy to be submerged in the background noise. In the signal preprocessing, the maximum correlation kurtosis deconvolution is used to denoise the signal, which can enhance the fault character...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G01M13/045
Inventor 李亚关晓艳
Owner KUNMING UNIV OF SCI & TECH