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CMT welding defect identification method based on acoustic signal multi-feature fusion

A multi-feature fusion and welding defect technology, which is applied in welding equipment, auxiliary welding equipment, character and pattern recognition, etc., to achieve the effect of accurate defect location, easy operation, and positioning defect location

Pending Publication Date: 2021-10-22
DALIAN JIAOTONG UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] In order to solve the above-mentioned problems existing in the existing welding defect identification method, the present invention provides a CMT welding defect identification method based on multi-feature fusion of acoustic signals

Method used

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  • CMT welding defect identification method based on acoustic signal multi-feature fusion
  • CMT welding defect identification method based on acoustic signal multi-feature fusion
  • CMT welding defect identification method based on acoustic signal multi-feature fusion

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Embodiment Construction

[0035] Embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but should not be used to limit the scope of the present invention.

[0036] The process flow of a CMT welding defect recognition method based on multi-feature fusion of acoustic signals of the present invention is as follows: figure 1 shown, including the following steps:

[0037] S1. Establish a system acquisition model, transmit the acoustic signal to the welding place, use the microphone and the acquisition card to collect the acoustic signal during the welding process,

[0038] BK4954 microphone can be used for the microphone, and the USB-4711A acquisition card can be used for the acquisition card. During the preprocessing of the acoustic signal, only the acoustic signal needs to be processed simply, and there is no need to process the acoustic signal too much...

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Abstract

The invention relates to the field of transportation equipment manufacturing, and particularly discloses a CMT welding defect identification method based on sound signal multi-feature fusion, and the method comprises the steps: taking a CMT welding defect as an analysis target in the analysis of a welding process signal, combining the existing data, and considering various conditions of faults and defects; adopting a plurality of mainstream signal analysis methods to carry out feature extraction such as wavelet transform, wavelet packet decomposition, Mel spectrum and short-time Fourier transform on sound signals; after feature extraction, fusing a plurality of signal features, and adopting a plurality of neural networks to carry out identification analysis on defect parts. Effective technical support is provided for analyzing welding acoustic signals so as to position defects, and the purpose of accurately positioning defect positions and defect types is achieved.

Description

technical field [0001] The invention relates to the field of transportation equipment manufacturing. Background technique [0002] CMT welding is the most widely used welding method in the rail transit equipment manufacturing industry. Welding defects are potential damage methods for workpiece fatigue fractures. Fatigue fractures caused by welding defects are highly concealed. Once they occur, they will lead to Catastrophic accidents cause serious economic losses. Therefore, accurate identification and location of defect parts when analyzing welding defects are of great significance to ensure the safe operation of rail vehicles. [0003] At present, the signal analysis of welding defects mainly obtains a qualitative evaluation of a single signal analysis through a certain signal analysis method, and lacks a comprehensive analysis of multiple angles. Therefore, how to construct the eigenvalue of multi-feature fusion to reveal the welding The location of defect occurrence is ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04B23K37/00G01N29/44
CPCB23K37/00G01N29/4481G06N3/045G06F2218/06G06F2218/08G06F2218/12G06F18/253Y02P90/30
Inventor 孙屹博龙海威杨光邹丽杨鑫华
Owner DALIAN JIAOTONG UNIVERSITY
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