Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Rolling bearing fault feature extraction method based on IITD and AMCKD

A rolling bearing and fault feature technology, applied in the field of fault diagnosis technology and signal processing analysis, can solve the problems of high endpoint effect index, distortion, and difficulty in extracting fault feature information.

Inactive Publication Date: 2019-10-11
KUNMING UNIV OF SCI & TECH
View PDF5 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a rolling bearing fault feature extraction method based on IITD and AMCKD, which is used to solve the problem that the rolling bearing fault signal is disturbed by noise and the fault feature information is difficult to extract, and the decomposed components in the original inherent time scale ITD algorithm appear Glitch and distortion, incomplete decomposition, high endpoint effect index, and difficulty in selecting filter length parameters of the maximum correlation kurtosis deconvolution MCKD denoising algorithm. Experiments show that the proposed method can extract rolling bearing fault features more effectively information

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
  • Rolling bearing fault feature extraction method based on IITD and AMCKD
  • Rolling bearing fault feature extraction method based on IITD and AMCKD
  • Rolling bearing fault feature extraction method based on IITD and AMCKD

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0071] Embodiment 1: as figure 1 As shown, a rolling bearing fault feature extraction method based on IITD and AMCKD, first use the IITD method to analyze the rolling bearing vibration signal X t Decompose to obtain a series of PR components; then use the cross-correlation coefficient or kurtosis criterion to filter the PR components that contain rich fault information for reconstruction, and perform AMCKD noise reduction processing on the reconstructed signal M(t); then use Teager -Kaiser Energy Operator (Teager-Kaiser Energy Operator, TKEO) demodulates the denoised signal H(t); finally performs fast Fourier FFT transform on the demodulated signal W(t), and analyzes the transformation According to the spectrum characteristics of the final signal Y(t), the characteristic frequency of the rolling bearing fault is extracted.

[0072] The specific steps of the method are as follows:

[0073] (1) Firstly, IITD decomposition is performed on the vibration signal of the rolling bea...

Embodiment 2

[0112] Example 2: This example uses the experimental data of the inner and outer rings of the rolling bearing in the electrical engineering laboratory of Case Western Reserve University in the United States as the data source for verification and analysis. The rolling bearing model of the experimental platform is 6205-2RS JEM SKF, and the specific bearing parameters are shown in Table 1. shown. The data collected for the experiment was collected by an accelerometer attached to the housing with a magnetic base. The accelerometers are placed at the 12 o'clock position on the drive end and fan end of the motor housing. The diameter of the bearing damage fault crack is 0.1778mm, and the crack depth is 0.2794mm. Load 2.237kW, frequency f z =2000r / min, the sampling frequency is 12kHz, and the number of sampling points is 4096. According to theoretical calculation, when there is a fault in the inner ring of the bearing, the fault fundamental frequency is 156.14Hz.

[0113] Bearin...

Embodiment 3

[0125] Embodiment 3: This example uses the method of the present invention to carry out experiments on rolling early weak faults, and uses the bearing fault data of the intelligent maintenance system of the University of Cincinnati (University of Cincinnati) disclosed by NASA to carry out experiments. The four bearings are driven by the motor, and the rotation speed of the bearings is f z =2000r / min, four double-row concave roller bearings of the type Rexnord ZA-2115 are installed on the horizontal shaft, and the specific parameters of the bearings are shown in Table 3. An acceleration sensor with a model number of PCB 353B33 is installed on each bearing to measure vibration signal data. The sampling frequency is 20kHz, and the number of sampling points is 20480. The data in the file "2nd_test" is to collect the vibration data of bearing 1 from normal operation to failure until the end of the failure experiment. The test platform has been running for nearly 7 days, and the ou...

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 relates to a rolling bearing fault feature extraction method based on the IITD and the AMCKD, belonging to the technical field of fault diagnosis and signal processing analysis. The rolling bearing fault feature extraction method based on the IITD and the AMCKD comprises the following steps: performing an IITD method decomposition operation on an original acquired fault vibration signal first to obtain a series of inherent rotational components; using a kurtosis index to select a PR component with more fault information features for reconstruction; then optimizing the length of areconstruction signal filter in the AMCKD algorithm by using a variable step size network search method; then performing noise reduction processing on the reconstructed signal by using the AMCKD algorithm; performing demodulation processing on the de-noised signal by using a Teager-Kaiser energy operator; and finally, analyzing the spectral features of the signal after an FFT transformation, so that the fault feature frequency information can be extracted. The rolling bearing fault feature extraction method based on the IITD and the AMCKD can effectively extract the fundamental frequency andfrequency doubling feature information of the rolling bearing fault, and has a better fault diagnosis effect.

Description

technical field [0001] The invention relates to a rolling bearing fault feature extraction method based on IITD and AMCKD, which belongs to the field of fault diagnosis technology and signal processing and analysis technology. Background technique [0002] With the continuous development of industrial production, the requirements for the safety performance of rotating machinery are getting higher and higher, and rolling bearings are important operating parts of rotating machinery, and are also one of the most vulnerable parts. Monitoring their operating status to ensure the safety of industrial production is extremely important. As far as practical engineering applications are concerned, due to the characteristics of nonlinear and non-stationary vibration signals of rolling bearings, the useful fault information is easily submerged due to the influence of noise. Therefore, the research on the fault feature extraction method of rolling bearings has always been a fault diagnos...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/045
CPCG01M13/045
Inventor 吴建德吴涛王晓东黄国勇范玉刚邹金慧冯早
Owner KUNMING UNIV OF SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com