Study on Volterra-SVM Model for Defect Recognition of Steel Plate

DENG Yong; HUANG Yuanwei; LAI Zhiyi

doi:10.13433/j.cnki.1003-8728.20200590

Volume 42 Issue 1

Jan. 2023

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Article Navigation > Mechanical Science and Technology for Aerospace Engineering > 2023 > 42(1): 132-138

DENG Yong, HUANG Yuanwei, LAI Zhiyi. Study on Volterra-SVM Model for Defect Recognition of Steel Plate[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(1): 132-138. doi: 10.13433/j.cnki.1003-8728.20200590

Citation:

DENG Yong, HUANG Yuanwei, LAI Zhiyi. Study on Volterra-SVM Model for Defect Recognition of Steel Plate[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(1): 132-138. doi: 10.13433/j.cnki.1003-8728.20200590

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Study on Volterra-SVM Model for Defect Recognition of Steel Plate

doi: 10.13433/j.cnki.1003-8728.20200590

1.
School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China
2.
Gas Transmission Management Office of Southwest Oil and Gas Field Company, Chengdu 610213, China

Received Date: 2021-03-17
Publish Date: 2023-01-25

Abstract

Abstract

Aiming at the problem of steel plate defect recognition, combined with the principle of ultrasonic pulse reflection, a steel plate defect recognition method based on Volterra series and Support Vector Machine (SVM) is proposed. Firstly, Volterra series model is used to construct the characteristic model of steel plate defects. Then, the feature parameters in the original signal, namely Volterra series kernel, are extracted by using the Fractional Order Particle Swarm Optimization (FO-PSO). Finally, the extracted feature vectors are input into the SVM model for training and testing to complete the classification and recognition of steel plate defects. Experiments were designed to obtain multiple sets of data samples for model validation. The experimental results show that the recognition model based on Volterra series and SVM can better complete the classification and recognition of steel plate defects, and the recognition accuracy is 93.3%.
- defect recognition,
- volterra series,
- fractional order particle swarm optimization algorithm,
- support vector machine

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References(20)

References

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