| 邓 勇,曹 敏,赖治屹.基于深度学习的天然气管道气体压力
超声检测模式识别方法[J].电子测量与仪器学报,2021,35(10):176-183 |
| 基于深度学习的天然气管道气体压力
超声检测模式识别方法 |
| Ultrasonic detection pattern recognition method for natural gaspipeline gas pressure based on deep learning |
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| DOI： |
| 中文关键词: 深度学习 超声检测 压力识别 卷积神经网络 变分模态分解 支持向量机 |
| 英文关键词:deep learning ultrasonic detection pressure identification convolutional neural network variational modal decomposition support vector machine |
| 基金项目:四川省科技支撑项目（2017FZ0033）资助 |
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| 中文摘要: |
| 针对天然气管道气体压力超声检测模式识别问题,提出了对原始信号进行预处理去除冗余信息,然后对信号进行变分
模态分解(variational modal decomposition,VMD)提取最优本征模态函数(intrinsic model functin,IMF)对信号进行重构,接着对处
理好的信号进行连续小波变换(continuous wavelet transform,CWT)使其成为高分辨的时频域 2 维图,最后用深度卷积神经网络
(Deep convolution neural network,DCNN)对图片进行特征提取并将部分网络输出和支持向量机( support vector machine,SVM)相
连实现有监督的学习和训练并用训练好的支持向量机对剩下的数据进行无监督的模式识别。 实验表明,VMD-CNN-SVM 模型
对压力有无的判别准确率为 90. 66%,相较于其他方法准确率最高。 |
| 英文摘要: |
| In order to solve the problem of pattern recognition of gas pressure detection in natural gas pipeline, the original signal is
preprocessed to remove redundant information, and then the signal is decomposed by variational mode decomposition to extract the
optimal Intrinsic mode function and reconstruct the signal. Then, the processed signal is transformed into a high-resolution twodimensional image in time and frequency domain by continuous wavelet transform. Finally, the image is extracted by deep convolution
neural network, and the output of part of the network is connected with support vector machine to realize supervised learning and
training. The trained support vector machine is used for unsupervised pattern recognition of the remaining data. Experiments show that
the accuracy of vmd-cnn-svm is 90. 66%, which is the highest compared with other methods. |
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