融合Welch功率谱和多尺度残差网络的油气管道缺陷诊断

doi:10.16265/j.cnki.issn1003-3033.2026.02.0599

摘要/Abstract

摘要：

为解决油气管道弯管检测信号受噪声污染导致缺陷分类准确率下降的问题,提出一种基于Welch功率谱特征增强和多头注意力改进型双分支多尺度-残差协同网络的油气弯管缺陷诊断模型。首先,利用基于汉明窗的Welch方法将采集到的时域信号转换为特征增强的功率谱,展示缺陷信号在不同频率处能量的分布情况;然后,并行堆叠的卷积层构成的多尺度网络分支负责提取信号功率谱的多维度特征,并利用多头注意力机制建立特征之间长期的关联,同时,残差网络分支捕捉信号功率谱的细节信息;最后,深度串联层融合双分支网络提取的特征,以实现缺陷分类。结果表明:在高噪声环境下,该模型的测试准确率为91.6%,相比于基于凯塞窗和平顶窗的模型,分类准确率提高1%~7.9%;相比于卷积神经网络(CNN)和长短时记忆网络(LSTM),准确率分别提高36.9%和10.3%。

关键词: Welch功率谱, 多尺度残差网络, 油气管道, 弯管缺陷, 高噪声, 智能诊断

Abstract:

To address the problem of reduced defect classification accuracy caused by noise contamination in the bend detection signals of oil and gas pipelines, this paper proposes an oil and gas elbow defect diagnosis model based on Welch power spectrum feature enhancement and multi-head attention improved dual-branch multi-scale-residual collaborative network. Firstly, the Welch method was used to convert the collected time domain signal into a feature-enhanced power spectrum, showing the energy distribution of the defect signals at different frequencies. Secondly, the multi-scale network branch composed of parallel stacked convolutional layers was responsible for extracting the multi-dimensional features of the signal power spectrum, and the multi-head attention mechanism was used to establish long-term associations between features. Simultaneously, the residual network branch captured the detail information of the signal power spectrum. Finally, the deep concatenation layer fused the features extracted by the dual-branch network to achieve defect classification. The experiment results show that in a high-noise environment, the test accuracy of the proposed model is 91.6%. Compared with the models based on Kaiser windows and flat-top windows, the classification accuracy is improved by 1%~7.9%; compared with convolutional neural network (CNN) and long short-term memory network (LSTM), the accuracy is improved by 36.9% and 10.3% respectively.

Key words: Welch power spectrum, multi-scale residual network, oil and gas pipeline, elbow defect, high noise, intelligent diagnosis

中图分类号:

X937

廖春燕, 梁伟, 刘双磊, 黄天长. 融合Welch功率谱和多尺度残差网络的油气管道缺陷诊断[J]. 中国安全科学学报, 2026, 36(2): 136-144.

LIAO Chunyan, LIANG Wei, LIU Shuanglei, HUANG Tianchang. Defect diagnosis method for oil and gas pipelines based on fusion of Welch power spectrum and multi-scale residual network[J]. China Safety Science Journal, 2026, 36(2): 136-144.

图/表 14

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图5

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表1

图7

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表2

不同窗函数下诊断模型在测试集上的评价结果

窗函数	评价指标
窗函数	精确率P	召回率R	$F 1$	准确率A
汉明窗	90.9	91.0	90.9	91.6
凯塞窗 $β = 0$	89.3	89.0	89.2	90.2
凯塞窗 $β = 5$	90.2	89.0	89.4	90.6
凯塞窗 $β = 8.6$	83.8	83.3	83.5	85.2
平顶窗	82.1	81.8	81.7	83.7

表2

图11

表3

不同模型在测试集上的评价结果

模型	评价指标
模型	精确率P	召回率R	$F 1$	准确率A
本文模型	90.9	91.0	90.9	91.6
CNN	45.8	51.8	47.9	54.7
LSTM	78.8	76.5	74.9	81.3

表3

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缺陷类别	样本个数	各数据集代表信号的信噪比/dB
缺陷类别	样本个数	数据集1	数据集2	数据集3	数据集4
腐蚀	448	2.94	2.00	1.06	-0.42
划痕	369	13.78	5.63	0.29	-6.58
焊缝开裂	572	18.09	6.58	1.71	-1.04
正常	645	18.21	8.82	0.66	-4.11