| 梁海波,王 怡.基于深度学习的天然气钢制管道缺陷检测方法研究[J].电子测量与仪器学报,2022,36(9):148-158 |
| 基于深度学习的天然气钢制管道缺陷检测方法研究 |
| Research on defect detection method of natural gassteel pipeline based on deep learning |
| |
| DOI： |
| 中文关键词: 超声检测 变分模态分解 卷积神经网络 随机森林 |
| 英文关键词:ltrasonic detection variational model decomposition convolutional neural network random forest |
| 基金项目:国家自然科学基金（52074233）项目资助 |
|
|
|
|
| 摘要点击次数: 968 |
| 全文下载次数: 2767 |
| 中文摘要: |
| 在天然气钢质管道腐蚀缺陷超声检测中,常规模式识别采用人工提取回波信号的方法,存在主观性强、普适性低的问
题。 基于此,本文提出用一维卷积神经网络(one-dimensional convolutional neural network,1D-CNN)提取回波信号的特征结合随
机森林(random forest RF) 分类的方法。 首先根据实验获取的回波信号的噪声情况,用小波包变换(wavelet packet transform
WPT)对信号进行去噪;并用变分模态分解( variational model decomposition VMD)对去噪后的信号进行分解和重构以获得平滑
的信号;最后将处理好的回波信号进行 1D-CNN 网络特征提取和随机森林分类。 实验结果表明,基于 VMD-1D-CNN-RF 的天然
气钢质管道缺陷检测方法针对人造缺陷的识别准确率为 85. 71%,针对天然气站场的管道缺陷识别准确率为 71. 05%,表明无需
专家识别也可初步判别管道状况。 |
| 英文摘要: |
| In the ultrasonic detection of corrosion defects of natural gas steel pipelines, the conventional pattern recognition method
adopts the method of manually extracting echo signals, which has the problems of strong subjectivity and low universality. Based on this,
this paper proposes a method to extract the features of echo signals by using one-dimensional convolutional neural network and classify the
features by combining with random forest. Firstly, according to the noise of the echo signal, the wavelet packet transform is used to
denoise the signal. The denoised signal is decomposed and reconstructed by variational modal decomposition to obtain a smooth signal.
Finally, the processed echo signals are extracted by 1D-CNN network features and classified by random forest. The experimental results
show that the identification accuracy of the method based on VMD-1D-CNN-RF is 85. 71% for artificial defects and 71. 05% for pipeline
defects in natural gas stations, indicating that the pipeline condition can be preliminarily identified without expert identification. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
