Gearbox fault recognition method based on improved empirical wavelet transform

An empirical wavelet and fault identification technology, which is applied in machine gear/transmission mechanism testing, mechanical component testing, machine/structural component testing, etc., can solve problems such as EWT method cannot be used, AM-FM signal over-decomposition, etc., to achieve It is beneficial to demodulation analysis, improves precision and accuracy, and has the effect of reasonable spectrum division

Active Publication Date: 2018-08-28
ZHEJIANG UNIV OF TECH
View PDF4 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] When the EWT method directly processes the vibration signal of the gearbox, the number of modal components needs to be preset, and in the process of dividing the spectrum by detecting the maximum value of the spectrum, it is easy to ...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Gearbox fault recognition method based on improved empirical wavelet transform
  • Gearbox fault recognition method based on improved empirical wavelet transform
  • Gearbox fault recognition method based on improved empirical wavelet transform

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0047] 1) Taking the collected vibration signal of the gearbox of the car seat horizontal drive (HDM) as an example, the time domain waveform is as follows image 3 As shown, the Fourier transform is performed on the time-domain signal, and the signal spectrum is obtained as Figure 4 shown;

[0048] 2) Using the EMD algorithm to decompose the frequency spectrum of the obtained HDM vibration signal, the obtained IMFs and residuals such as Figure 5 shown;

[0049] 3) Reconstruct the last 5 IMFs and residuals (that is, the sum of IMF10~IMF14 and the residual res), and obtain their spectral trends (for clear display, the amplitude of the spectral trends is multiplied by 2) and the Fourier of the vibration signal leaf spectrum such as Figure 6 shown;

[0050] 4) carry out the detection of the maximum value to the frequency spectrum trend, the number of the maximum value is 11, make N=11, and adopt the local minimum and maximum value to carry out the boundary detection to the f...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a gearbox fault recognition method based on an improved empirical wavelet transform. The method mainly comprises the following steps: carrying out spectrum trend-improved empirical wavelet transform decomposition on vibration signals of an automobile seat horizontal driver gearbox to obtain various mode components; carrying out demodulation analysis on each mode component to obtain characteristic frequency; and comparing the characteristic frequency with rotation frequency of each gear in the gearbox to realize gearbox fault diagnosis. According to the technical schemeabove, with the advantages of high adaptability of EMD and strict WT theory being fully combined, frequency spectrum division is allowed to be more reasonable, each mode obtained after decomposition basically has no over-decomposition phenomenon, and the obtained each mode is more conducive to demodulation analysis, thereby improving gearbox fault detection precision and accuracy, and well solvingthe problem of need of presetting mode number in empirical wavelet transform and the problem of over-decomposition caused since spectrum division is too concentrated in the frequency band with largeamplitude in the signal spectrum.

Description

technical field [0001] The invention belongs to the technical field of gearbox fault identification, and in particular relates to a gearbox fault identification method based on improved empirical wavelet transform. Background technique [0002] The gearbox vibration signal has the characteristics of complex multi-component and AM-FM. The amplitude demodulation and frequency demodulation methods can avoid the complex sideband analysis in the traditional Fourier spectrum and effectively identify the fault characteristic frequency. However, a prerequisite for effective demodulation analysis of multi-component AM-FM signals is to extract effective modal components. Both wavelet transform (Wavelet Transform, WT) and empirical mode decomposition (Empirical Mode Decomposition, EMD) are widely used. state component extraction method. But WT has the problem of the choice of wavelet base and the lack of adaptability after the wavelet base is determined, while EMD is an empirical meth...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G01M13/02
CPCG01M13/021G01M13/028
Inventor 张征王昌明鲍雨梅吴化平李吉泉丁浩
Owner ZHEJIANG UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap