Multi-resolution singular-spectrum entropy and SVM based leakage acoustic emission signal identification method

An acoustic emission signal, singular spectrum entropy technology, used in character and pattern recognition, instruments, computer parts, etc.

Inactive Publication Date: 2016-04-13
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

Although there are many documents in the field of acoustic emission detection, the current research on acoustic emission technology for pipeline leakage is mainly to determine

Method used

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  • Multi-resolution singular-spectrum entropy and SVM based leakage acoustic emission signal identification method
  • Multi-resolution singular-spectrum entropy and SVM based leakage acoustic emission signal identification method
  • Multi-resolution singular-spectrum entropy and SVM based leakage acoustic emission signal identification method

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Embodiment 1

[0041] Embodiment 1: as Figure 1-9 As shown, the leakage AE signal recognition method based on multi-resolution singular spectral entropy and SVM first uses a digital AE system to collect experimental data, and collects three kinds of simulated AE signals: knocking, sandpaper and lead breaking; then the collected The acoustic emission signal is subjected to wavelet multi-scale decomposition; then the singular spectral entropy of each layer is obtained, and it is formed into a feature vector; finally, the feature vector is divided into a training set and a test set, and the training set is used for training to obtain a support vector machine classification Using the trained classifier to classify the test set, the result is the classification and recognition of different acoustic emission signals.

[0042] The specific steps of the leakage acoustic emission signal recognition method based on multi-resolution singular spectral entropy and SVM are as follows:

[0043] A. Acoust...

Embodiment 2

[0051] Embodiment 2: as Figure 1-9 As shown, the leakage AE signal recognition method based on multi-resolution singular spectral entropy and SVM first uses a digital AE system to collect experimental data, and collects three kinds of simulated AE signals: knocking, sandpaper and lead breaking; then the collected The acoustic emission signal is subjected to wavelet multi-scale decomposition; then the singular spectral entropy of each layer is obtained, and it is formed into a feature vector; finally, the feature vector is divided into a training set and a test set, and the training set is used for training to obtain a support vector machine classification Using the trained classifier to classify the test set, the result is the classification and recognition of different acoustic emission signals.

[0052] The specific steps of the leakage acoustic emission signal recognition method based on multi-resolution singular spectral entropy and SVM are as follows:

[0053] A. Acoust...

Embodiment 3

[0062] Embodiment 3: as Figure 1-9 As shown, the leakage acoustic emission signal recognition method based on multi-resolution singular spectral entropy and SVM first adopts the SAEU2S digital acoustic emission system produced by Beijing Shenghua Xingye Technology Co., Ltd. to collect experimental data. 20 groups of simulated acoustic emission signals; then wavelet multi-scale decomposition is performed on the collected acoustic emission signals; then the singular spectrum entropy of each layer is obtained, and a feature vector is formed; finally, the feature vector is divided into training set and The test set and the training set are used for training to obtain the parameters of the support vector machine classifier, and the trained classifier is used to classify the test set, and the result is the classification and recognition of different acoustic emission signals.

[0063] The specific steps of the leakage acoustic emission signal recognition method based on multi-resol...

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Abstract

The invention relates to a multi-resolution singular-spectrum entropy and SVM based leakage acoustic emission signal identification method, and belongs to the technical field of acoustic emission signal mode identification. The method comprises the steps of firstly, performing experimental data acquisition by adopting a digital acoustic emission system to acquire three simulated acoustic emission signals of knocking, abrasive paper and lead breaking; secondly, performing wavelet multi-scale decomposition on the acquired acoustic emission signals; thirdly, calculating singular-spectrum entropies of all layers and combining the singular-spectrum entropies into an eigenvector; and finally, dividing the eigenvector into a training set and a test set, wherein the training set is used for training to obtain SVM classifier parameters, and the trained classifier is used to perform classification testing on the test set, so that different acoustic emission signals can be identified by classification. According to the method, features of various samples can be well described by adopting a feature extraction method combining the wavelet multi-scale decomposition with the multi-resolution singular-spectrum entropy, and the identification method adopting the SVM has higher identification rate for the acoustic emission signals.

Description

technical field [0001] The invention relates to a leakage acoustic emission signal identification method based on multi-resolution singular spectrum entropy and SVM, and belongs to the technical field of acoustic emission signal pattern identification. 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. Once the leak point is not handled in time, it is easy to Industrial accidents occur, resulting in serious economic losses and casualties. Acoustic emission detection technology is a detection technology that uses transient elastic waves generated by rapid energy release of local materials as an excitation source, and plays an important role in nondestructive testing. At present, acoustic emission detection has achieved certain research results in tool w...

Claims

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Application Information

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