Circuit breaker voiceprint and vibration entropy feature fusion fault diagnosis method

A technology of feature fusion and fault diagnosis, applied in the testing of machines/structural components, instruments, genetic rules, etc., can solve the problem of insufficient diagnostic accuracy, improve generalization performance, reduce misdiagnosis and missed diagnosis, and improve accuracy Effect

Active Publication Date: 2020-08-04
GUANGXI POWER GRID ELECTRIC POWER RES INST
View PDF8 Cites 11 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the traditional feature processing method is to mechanically combine the acoustic and vibration signal

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
  • Circuit breaker voiceprint and vibration entropy feature fusion fault diagnosis method
  • Circuit breaker voiceprint and vibration entropy feature fusion fault diagnosis method
  • Circuit breaker voiceprint and vibration entropy feature fusion fault diagnosis method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0060] see figure 1 , figure 1 It is a schematic flow chart of a fault diagnosis method for fusion of circuit breaker voiceprint and vibration entropy features in the implementation of the present invention.

[0061] Such as figure 1 As shown, a fault diagnosis method for circuit breaker voiceprint and vibration entropy feature fusion, the method includes:

[0062] S11: Perform band-pass filtering on the sound signal, and obtain roughness, box dimension, directionality, contrast, linearity and regularity reflecting time-frequency texture characteristics through generalized S-transformation;

[0063] In the specific implementation process of the present invention, the band-pass filtering is carried out to the sound signal, and through the generalized S transform, the roughness, box dimension, directionality, contrast, linearity and regularity reflecting the time-frequency texture characteristics include: According to the frequency band characteristics of the sound signal of ...

specific Embodiment approach

[0126] In the specific implementation, taking the ZN65-12 circuit breaker as an example, the fault diagnosis process is as follows figure 2 shown, with figure 2 Show the circuit breaker fault diagnosis flowchart in the implementation of the present invention; The specific embodiment of the present invention is as follows:

[0127] Step 1, use AC144 piezoelectric acceleration sensor (0.6-10000Hz) and NVL-AF-aduio to embed waterproof (storm) high-fidelity pickup (20-20000Hz), in which the vibration sensor is adsorbed to the circuit breaker body, and the sound sensor is placed 50cm away from the sound source; collect multiple sets of sound wave and vibration sample signals under the circuit breaker mechanism jamming, loose base, connecting rod falling off, voltage fluctuation and normal state;

[0128] In the second step, the frequency band noise of the sound signal below 3kHz and above 15kHz is filtered out by a finite-length unit impulse response (FIR) bandpass filter, and t...

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 circuit breaker voiceprint and vibration entropy feature fusion fault diagnosis method. The method comprises the steps of carrying out band-pass filtering on a sound signal,and obtaining the roughness, box dimension, direction degree, contrast ratio, linearity and regularity which reflect time-frequency texture features through generalized S transformation; constructingvoiceprint features based on the roughness, the box dimension, the direction degree, the contrast ratio, the linearity and the regularity which reflect the time-frequency texture features; selecting parameters for a vibration signal, and obtaining permutation entropy through variational mode decomposition; constructing vibration entropy features based on the permutation entropy; performing deep fusion based on the voiceprint features and the vibration entropy features to obtain a joint feature vector; performing learning training of an SVM model on the joint feature vector; and based on the GWO-SVM diagnosis model, optimizing a penalty factor and a mixing coefficient in the SVM model by adopting the GWO model. In the embodiment of the invention, the influence of sensor frequency response and environmental noise on the fault diagnosis of the circuit breaker is reduced.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of electrical equipment, in particular to a fault diagnosis method for fusion of circuit breaker voiceprint and vibration entropy features. Background technique [0002] As an important control and protection device in the power system, the high-voltage circuit breaker can directly affect the safety and stability of the power system. Therefore, the reliability of the high-voltage circuit breaker is very important to the protection and control of the power grid. [0003] The characteristics of the vibration signal can be used to diagnose the mechanical failure of the circuit breaker, which is limited to the charge accumulation effect and coupling mode of the piezoelectric acceleration sensor, but in practical applications, there will be saturation when the amplitude is large, and it is easy to produce high-frequency shocks caused by the charge accumulation effect invalidated. The acoustic ...

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): G01M13/00G06K9/62G06N3/12
CPCG01M13/00G06N3/126G06F18/253
Inventor 夏小飞黄辉敏陈庆发雷一鸣吕泽承
Owner GUANGXI POWER GRID ELECTRIC POWER RES INST
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