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Leakage sound emission signal identification method based on multi-scale morphological decomposition energy spectrum entropy and support vector machine

An acoustic emission signal and support vector machine technology, which is applied in character and pattern recognition, computer parts, instruments, etc., can solve the problems of detection and classification of leaked acoustic emission signals, leaked acoustic emission signals, etc., and achieves strong resolution ability and correct identification. high rate effect

Inactive Publication Date: 2016-04-06
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

[0007] The present invention provides a leakage acoustic emission signal identification method based on multi-scale morphological decomposition energy spectrum entropy and support vector machine, which is used to solve the problem of detection and classification of leakage acoustic emission signals

Method used

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  • Leakage sound emission signal identification method based on multi-scale morphological decomposition energy spectrum entropy and support vector machine
  • Leakage sound emission signal identification method based on multi-scale morphological decomposition energy spectrum entropy and support vector machine
  • Leakage sound emission signal identification method based on multi-scale morphological decomposition energy spectrum entropy and support vector machine

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Embodiment 1

[0038] Embodiment 1: as Figure 1-7 As shown, a leakage AE signal recognition method based on multi-scale morphological decomposition energy spectrum entropy and support vector machine, first adopts the digital acoustic emission system to collect experimental data; conducts multi-scale morphological decomposition on the collected analog leakage AE signal, respectively Calculate its spectral energy at different scales, and calculate the energy spectrum entropy value; then calculate the proportion of each scale to the energy spectrum entropy, and form it into a feature vector; finally use the support vector machine to train and test the feature vector.

[0039] The concrete steps of described method are as follows:

[0040] Step1. Acoustic emission signal acquisition: collect N groups of analog leakage acoustic emission signals through the digital acoustic emission system, and the signal is recorded as f(x);

[0041] Step2, the structural element adopts the flat structural elem...

Embodiment 2

[0046] Embodiment 2: as Figure 1-7 As shown, a leakage AE signal recognition method based on multi-scale morphological decomposition energy spectrum entropy and support vector machine, first adopts the digital acoustic emission system to collect experimental data; conducts multi-scale morphological decomposition on the collected analog leakage AE signal, respectively Calculate its spectral energy at different scales, and calculate the energy spectrum entropy value; then calculate the proportion of each scale to the energy spectrum entropy, and form it into a feature vector; finally use the support vector machine to train and test the feature vector.

[0047] The concrete steps of described method are as follows:

[0048] Step1. Acoustic emission signal acquisition: collect N groups of analog leakage acoustic emission signals through the digital acoustic emission system, and the signal is recorded as f(x);

[0049] Step2, the structural element adopts the flat structural elem...

Embodiment 3

[0053] Embodiment 3: as Figure 1-7 As shown, a leakage AE signal recognition method based on multi-scale morphological decomposition energy spectrum entropy and support vector machine, first adopts the digital acoustic emission system to collect experimental data; conducts multi-scale morphological decomposition on the collected analog leakage AE signal, respectively Calculate its spectral energy at different scales, and calculate the energy spectrum entropy value; then calculate the proportion of each scale to the energy spectrum entropy, and form it into a feature vector; finally use the support vector machine to train and test the feature vector.

[0054] The simulated leakage acoustic emission signal is selected from any two or more of the simulated leakage acoustic emission signals of knocking, sandpaper and lead breaking.

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Abstract

The invention relates to a leakage sound emission signal identification method based on a multi-scale morphological decomposition energy spectrum entropy and a support vector machine and belongs to the sound emission signal mode identification field. In the invention, firstly, a digital sound emission system is used to carry out experiment data acquisition, multi-scale morphological decomposition is performed on collected simulated leakage sound emission signals, spectrum energy on different scales is calculated respectively and an power spectrum entropy value is calculated; and then, a proportion of each scale in the power spectrum entropy is calculated so as to form a characteristic vector; and finally, the support vector machine is used to train and test the characteristic vector. By using the method, knocking, abrasive paper and lead breaking signals can be simultaneously distinguished, an identification correct rate is high and automatic classification can be realized. Through calculating a multi-scale decomposition power spectrum entropy value of a sound emission signal and calculating the proportion of each scale in the power spectrum entropy, sound emission signal state information can be well reflected and the state information can be taken as the characteristic vector of the sound emission signal.

Description

technical field [0001] The invention relates to a leakage acoustic emission signal recognition method based on multi-scale morphological decomposition energy spectrum entropy and support vector machine, belonging to the field of acoustic emission signal pattern recognition. Background technique [0002] Today, with the rapid development of industry, all kinds of pressure pipes and high-pressure boilers can be seen everywhere. During use, due to corrosion, wear and other reasons, the pipe or furnace wall material may be damaged and cause leakage. Leakage detection is an important tool for petroleum, chemical, natural gas and urban water supply. One of the important problems to be solved in this field. Studying the detection theory and detection methods of leakage signals, and realizing the classification and identification of leakage signals, has important theoretical value and practical significance for maintaining the safe operation of pipelines and boilers and avoiding was...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06F2218/08G06F2218/12G06F18/2411
Inventor 张寿明于蕊
Owner KUNMING UNIV OF SCI & TECH
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