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

Rotating machinery small sample fault diagnosis method based on generative adversarial network

A technology for rotating machinery and fault diagnosis, applied in biological neural network models, neural learning methods, testing of mechanical parts, etc.

Pending Publication Date: 2022-02-25
TECH & ENG CENT FOR SPACE UTILIZATION CHINESE ACAD OF SCI +1
View PDF0 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The object of the present invention is to provide a kind of small-sample fault diagnosis method of rotating machinery based on generation confrontation network, thereby solving the aforementioned problems existing in the prior art

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
  • Rotating machinery small sample fault diagnosis method based on generative adversarial network
  • Rotating machinery small sample fault diagnosis method based on generative adversarial network
  • Rotating machinery small sample fault diagnosis method based on generative adversarial network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0064] figure 2 It is an implementation flowchart of an embodiment of the small-sample fault diagnosis method of rotating machinery based on generative confrontation network in the present invention. Such as figure 2 As shown, the present invention is based on the specific steps of the rotating machinery small sample fault diagnosis method of generation confrontation network comprising:

[0065] S1: Use the vibration acceleration sensor to collect the vibration signal of the rotating machinery.

[0066] S2: Carry out data preprocessing on the collected vibration signals of rotating machinery, construct an original data set, and divide the original data set into a training set and a test set.

[0067] S3: Build a network model based on ACWGAN-GP-ARGMAX.

[0068] S4: Carry out model training on the ACWGAN-GP-ARGMAX-based network model, and integrate the trained network model in each health state to construct a diagnostic model.

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 rotating machinery small sample fault diagnosis method based on a generative adversarial network. The method comprises the following steps: firstly, converting an acquired rotating machinery time domain signal into a frequency spectrum signal of which the characteristics are easy to observe through fast Fourier transform (FFT); and then, on the basis of the ACWGAN-GP, introducing an 'argmax' multi-classification thought so that an ACWGAN-GP-ARGMAX diagnosis model is obtained. According to the diagnosis model, a discriminator is endowed with classification and recognition capability, the classification and recognition capability of the discriminator is enhanced, and the accuracy and efficiency of fault diagnosis of the small sample of the rotating machinery can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of rotating machinery, in particular to a fault diagnosis method for small samples of rotating machinery based on generative confrontation network. Background technique [0002] Rotating machinery is widely used in aerospace, shipbuilding, wind power, petrochemical and other fields. It is one of the key components in industrial scenarios, and it is also a kind of equipment that is prone to failure. Most of the rotating machinery is a cyclic symmetrical structure, which is stable under normal working conditions. However, when the rotating machinery fails, its symmetry is broken, resulting in unstable performance and prone to major accidents. In order to avoid catastrophic failure of rotating machinery, it is of great significance to continuously monitor and diagnose its operating status. [0003] The operating status of rotating machinery can usually be detected and diagnosed by measuring...

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): G06K9/00G06K9/62G06N3/04G06N3/08G01M13/00G01H17/00
CPCG06N3/086G01M13/00G01H17/00G06N3/045G06F2218/08G06F2218/12G06F18/24G06F18/214
Inventor 曹智伏洪勇王珂李振祥张俊华
Owner TECH & ENG CENT FOR SPACE UTILIZATION CHINESE ACAD OF SCI
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