Improved self-adaptive sparse sampling fault classification method

A sparse sampling and fault classification technology, applied in the fields of instrumentation, calculation, mechanical bearing testing, etc., can solve problems such as rotating machinery fault classification, and achieve the effect of solving time-shift deviation, reducing redundant information, and improving computing efficiency.

Inactive Publication Date: 2019-07-09
BEIJING UNIV OF CHEM TECH
View PDF4 Cites 25 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to propose an improved adaptive sparse sampling fault classification method to solve the fault classification problem of rotating machinery

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
  • Improved self-adaptive sparse sampling fault classification method
  • Improved self-adaptive sparse sampling fault classification method
  • Improved self-adaptive sparse sampling fault classification method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0046] (1) Acquisition of vibration signals: The vibration signals of bearings are collected by means of the rotating machinery failure simulation experiment platform. The bearing defects, sampling frequency, spindle speed, and signal length can be set by yourself. In the case of the same sampling frequency and spindle speed, the vibration signals of normal bearing, inner ring fault, outer ring fault, and rolling element fault are collected respectively, and four types of signal test samples and training samples are respectively constructed, and the length of each group of signals is N. Normalize the signal to ensure the unity of signal amplitude and magnitude;

[0047] (2) Feature enhancement: use wavelet modulus maxima to perform sparse representation feature enhancement processing on training sample signals and test sample signals, and extract fault features. The training sample signal and the test sample signal are decomposed by t-layer wavelet respectively to obtain the w...

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

An improved self-adaptive sparse sampling fault classification method belongs to the technical field of fault diagnosis. A traditional sparse classification method is improved. Firstly, a wavelet module maximum value and a kurtosis method are used for carrying out feature enhancement processing on signals, and on the premise that signal sparsity is guaranteed, a unit matrix is adopted to replace aredundant dictionary. Secondly dimension reduction is carried out on data by adopting a Gaussian random measurement matrix, thereby reducing redundant information in the signal, and reserving effective and small amount of data. Then, a sparse coefficient is solved by adopting a sparsity adaptive matching pursuit (SAMP) algorithm, and the compressed signal is reconstructed; and finally, a cross correlation coefficient is adopted as a judgment basis of the category of the fault, so that an improved adaptive sparse sampling fault classification method is provided. Experimental verification proves that redundant information in signals is effectively reduced, the influence of time shift deviation on fault type judgment is avoided, meanwhile, the operation complexity is reduced, and the calculation speed and the reconstruction precision are improved.

Description

technical field [0001] The invention relates to a rotating machinery fault classification method, in particular to an improved self-adaptive sparse sampling fault classification method, which belongs to the technical field of fault diagnosis. Background technique [0002] The fault diagnosis of rotating machinery is of great significance to the safe operation of equipment. When the mechanical equipment fails, its vibration signal will change suddenly. Therefore, the fault diagnosis method based on vibration signal is a method widely used in the field of fault diagnosis. The compressed sensing theory can realize the compressed sampling of the signal without being restricted by the Shannon sampling theorem, reduce the redundant information in the signal, effectively reduce the pressure of data storage and transportation, and effectively realize the diagnosis of mechanical equipment faults with a small amount of data . [0003] The meaning of sparse representation theory is to...

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): G06K9/00G06K9/46G01M13/04
CPCG01M13/04G06V10/40G06V10/513G06F2218/06G06F2218/08G06F2218/12
Inventor 王华庆卞英婕任帮月李天庆陈学斌
Owner BEIJING UNIV OF CHEM 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