| 基于包络和极值约束的超声混叠信号稀疏分解 |
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| 引用本文： | 王宪,罗彬杰,蒋音盈. 基于包络和极值约束的超声混叠信号稀疏分解[J]. 上海第二工业大学学报, 2023, 40(4): 351-358 |
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| 作者姓名： | 王宪 罗彬杰 蒋音盈 |
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| 作者单位： | 1. 上海第二工业大学高等职业技术(国际) 学院, 上海201209;,2. 华东理工大学信息科学与工程学院, 上海200237,2. 华东理工大学信息科学与工程学院, 上海200237 |
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| 基金项目： | 国家自然科学基金项目(51875350) 资助 |
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| 摘 要： | 稀疏分解由于其在解决信号混叠问题中的出色效果,近年来常被用于多层焊接缺陷的检测中。此类方法的第1个关键步骤是对一维超声数据进行分割,以对主要信号特征进行识别和处理。基于正交匹配追踪算法,对超声检测信号使用Hilbert包络检波绘制信号上包络,同时引入极值约束条件过滤噪声,利用工件参数进一步实现对目标界面波信号段的自适应截取。该方法降低了人工预处理成本,提升有效目标信号截取准确性,从而有助于后续的稀疏分解。经过对实测信号的实验分析,证明了该方法能够准确定位目标信号,消除干扰波形影响,同时提升了超声信号稀疏分解的效果。
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| 关 键 词： | 缺陷检测 信号混叠 包络分割 稀疏分解 |
Sparse Decomposition of Ultrasonic Mixed Signals Based on Envelope andExtremum Constraints |
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| WANG Xian,LUO Bin-jie and JIANG Yin-ying. Sparse Decomposition of Ultrasonic Mixed Signals Based on Envelope andExtremum Constraints[J]. Journal of Shanghai Second Polytechnic University, 2023, 40(4): 351-358 |
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| Authors: | WANG Xian LUO Bin-jie JIANG Yin-ying |
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| Affiliation: | 1. School of Higher Vocational and Technical (International), Shanghai Polytechnic University, Shanghai 201209,China;,2. School of Information Science and Engineering, East China University of Science and Technology,Shanghai 200237, China and 2. School of Information Science and Engineering, East China University of Science and Technology,Shanghai 200237, China |
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| Abstract: | Sparse decomposition has been frequently used in recent years for the detection of multilayer weld defects due to its excellent results in solving signal aliasing problems. The first key step in such methods is the segmentation of the one-dimensional ultrasonic data for the identification and processing of the main signal features. Based on the orthogonal matching tracking algorithm, the ultrasonicdetection signal is plotted using Hilbert envelope detection to plot the upper envelope of the signal, while introducing extreme value constraints to filter the noise. The adaptive interception of the target interface wave signal segment is further realized by using the workpiece parameters. The method reduces the cost of manual pre-processing and improves the accuracy of effective target signalinterception, thus contributing to the sparse decomposition. After the experimental analysis of the measured signals, it is proved that the method can accurately locate the target signal, eliminate the influence of interference waveform, and improve the effect of sparse decomposition of ultrasonic signal. |
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| Keywords: | defect detection signal aliasing envelope segmentation sparse decomposition |
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