Self-adaptive stochastic resonance early fault diagnosis method based on grey wolf optimization algorithm

A technology of stochastic resonance and optimization algorithm, applied in calculation, calculation model, mechanical bearing testing, etc., can solve problems such as low accuracy of early weak fault diagnosis, difficulty in simultaneous adaptive selection of multiple parameters, and little consideration of parameter interaction, etc. Achieve the effect of strong weak signal detection ability, avoiding major safety accidents and wide application range

Active Publication Date: 2017-08-22
SICHUAN UNIV
View PDF4 Cites 16 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the stochastic resonance method is greatly affected by its structural parameters, and it is difficult to obtain ideal detection results in the actual signal processing process.
Most of the existing adaptive stochastic resonance methods sel

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
  • Self-adaptive stochastic resonance early fault diagnosis method based on grey wolf optimization algorithm
  • Self-adaptive stochastic resonance early fault diagnosis method based on grey wolf optimization algorithm
  • Self-adaptive stochastic resonance early fault diagnosis method based on grey wolf optimization algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0037] The present invention introduces the gray wolf optimization algorithm into the bistable stochastic resonance method, optimizes the stochastic resonance structural parameters, and adaptively selects the best structural parameters according to the characteristics of the input signal, realizes the best stochastic resonance output, and then realizes weak fault characteristics Accurate extraction and accurate identification of faults. Specific steps are as follows:

[0038] Step 1: Obtain the original vibration signal;

[0039] Step 2: Carry out linear compression preprocessing on the original signal (the compression ratio is denoted as R), so that it meets the small parameter requirement of stochastic resonance, that is, the signal frequency is much smaller than 1 (f 0 <<1);

[0040] Step 3: Parameter initialization. Specify the optimization range [l,u] of bistable stochastic resonance structure parameters a and b, the number of gray wolf populations N, the maximum numbe...

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 the field of early fault diagnosis of rotating machineries, and discloses a self-adaptive stochastic resonance early fault diagnosis method based on a grey wolf optimization algorithm. The self-adaptive stochastic resonance early fault diagnosis method improves the weak signal detection ability of a stochastic resonance method, and realizes precise diagnosis of machinery early faults. The self-adaptive stochastic resonance early fault diagnosis method introduces the grey wolf optimization algorithm into a bistable state stochastic resonance method, optimizes stochastic resonance structure parameters, selects the optimal structure parameter adaptively according to input signal features, realizes the optimal stochastic resonance output, and further achieves precise extraction of weak faults and accurate fault recognition. The self-adaptive stochastic resonance early fault diagnosis method is suitable for early fault diagnosis of rotating machinery.

Description

technical field [0001] The invention relates to the field of early fault diagnosis of rotating machinery, in particular to a bistable self-adaptive stochastic resonance early fault diagnosis method based on gray wolf optimization algorithm. Background technique [0002] As the key large-scale modern rotating machinery equipment in the pillar enterprises of the national economy, its safe operation is not only related to the life safety of equipment operators, the economic interests of enterprises, but also to national security and rights and interests. A large number of scientific research and engineering examples show that if the fault characteristics can be effectively extracted in the early stage of the fault, and then effective targeted remedial measures can be formulated to ensure the safe and efficient operation of the equipment. [0003] However, large rotating machinery usually works in a low-speed, heavy-load, and strong-noise environment, resulting in the vibration ...

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): G01M99/00G01M13/04G06N3/00
CPCG01M13/045G01M99/005G06N3/006
Inventor 苗强张新刘志汶王磊张恒孙冬宁
Owner SICHUAN UNIV
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