基于一维卷积神经网络的超声导波管道裂纹识别方法

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (5) : 183-189.

论文

唐若笠，张尚煜，伍文君

作者信息 +

Crack identification method of ultrasonic guided wave pipeline based on MS-1D CNN

TANG Ruoli, ZHANG Shangyu, WU Wenjun

Author information +

文章历史 +

摘要

在船舶运输、石油化工等需要广泛使用各类型管道的行业中，管道的结构健康监测(SHM)对于工业系统的安全稳定运行意义重大。在基于超声导波的管道裂纹等级识别方面，本文建立了一个与实际管道基本一致的有限元模型，通过添加噪声的方式合成了更接近实际检测的导波数据。基于包含不同管道裂纹等级的有限元仿真数据库，本文提出了一种基于多尺度一维卷积神经网络(MS-1DCNN)的管道裂纹等级识别模型，该模型以端到端的方法，将原始波形信号直接作为输入，无需专门设计信号降噪及特征提取算法。实验结果表明，该模型相较于传统机器学习方法在噪声环境下对管道裂纹等级的识别具有较高精度，并通过实物管道实验，验证了该模型在管道结构健康监测中的有效性。

Abstract

In industries that require extensive use of various types of pipelines such as ship transportation and petrochemical industry, structural health monitoring (SHM) of pipelines is of great significance for the safe and stable operation of industrial systems. In terms of pipeline crack grade identification based on ultrasonic guided wave, this paper constructs the finite element simulation model of the actual pipeline object, and synthesizes guided wave data closer to the actual detection by adding noise. Based on the finite element simulation sample database containing different pipeline crack grades, a pipeline crack grade recognition model based on multi-scale one-dimensional convolution neural network (MS-1DCNN) is proposed in this paper. The model realizes the direct use of the original waveform signal as the model input in an end-to-end manner, without the need for special design of signal noise reduction and feature extraction algorithm. The experimental results show that the model has higher accuracy than the traditional machine learning method in identifying pipeline crack grade in noise environment. In addition, this paper builds an experimental platform for crack identification based on physical pipelines, which further verifies the effectiveness of the proposed models and methods in pipeline structural health monitoring.

导出引用

唐若笠，张尚煜，伍文君. 基于一维卷积神经网络的超声导波管道裂纹识别方法[J]. 振动与冲击, 2023, 42(5): 183-189

TANG Ruoli, ZHANG Shangyu, WU Wenjun. Crack identification method of ultrasonic guided wave pipeline based on MS-1D CNN[J]. Journal of Vibration and Shock, 2023, 42(5): 183-189

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