Reconstructing echoes completely submerged in background noise by a stacked denoising autoencoder method for low-power EMAT testing

电磁声换能器 声学 降噪 信号(编程语言) 计算机科学 噪音(视频) 功率(物理) 自编码 人工神经网络 材料科学 人工智能 超声波检测 超声波传感器 物理 图像(数学) 程序设计语言 量子力学
作者
Jinjie Zhou,Dianrui Yu,Xiang Li,Yang Zheng,Yao Liu
出处
期刊:Measurement Science and Technology [IOP Publishing]
卷期号:34 (12): 125910-125910
标识
DOI:10.1088/1361-6501/acf23c
摘要

Abstract Low-power electromagnetic-acoustic transducer (EMAT) is crucially important for safety-critical equipment in industry, especially for potential explosives and inflammable petrochemical equipment and facilities. When the excitation power is very low, the corresponding echoes are overwhelmed in noise and related measurement would be inaccurate. To solve this problem, this paper presents a new echo reconstruction method based on a deep stacked denoising autoencoder (DSDAE) for nondestructive evaluation. First, the uses of reference signals and new data structure are to improve the training efficiency. A hybrid method based on variational mode decomposition and wavelet transform is used to obtain clean reference signals as inputs of the deep network. Then, the modified network structure and loss function aim to improve the ability of feature extraction and reconstruct clean echoes from low-power EMAT signals. To validate the effectiveness of the proposed method, the experiments of self-excitation and receiving A-scan inspections of stepped specimens with different thicknesses are conducted at some excitation voltages, as low as 25 V. The results indicate that the proposed DSDAE shows better and more stable denoising performance than some popular processing methods for different specimens and excitation voltages. It greatly improves the signal-to-noise ratio of the reconstructed signal to 20 dB. When applying to thickness measurement of specimens, its relative error is lower than 0.3%, which provides a practical and accurate tool for low-power EMAT testing.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
微不足道完成签到,获得积分10
刚刚
Marita完成签到,获得积分10
2秒前
梦哈哈发布了新的文献求助10
3秒前
微不足道发布了新的文献求助10
3秒前
英姑应助犹豫的铅笔采纳,获得10
4秒前
shirai发布了新的文献求助10
4秒前
华仔完成签到,获得积分10
5秒前
仰望星空发布了新的文献求助10
5秒前
5秒前
飞呀发布了新的文献求助30
6秒前
hellomoon完成签到 ,获得积分10
6秒前
噜啦啦啦发布了新的文献求助10
8秒前
9秒前
SYLH应助俏皮的白柏采纳,获得10
9秒前
10秒前
当归完成签到,获得积分10
10秒前
田様应助虚心的芹采纳,获得10
10秒前
10秒前
灿cancan发布了新的文献求助10
11秒前
隐形的绮烟完成签到,获得积分10
11秒前
liu完成签到,获得积分10
12秒前
12秒前
Shine完成签到 ,获得积分10
12秒前
活力青筠发布了新的文献求助10
13秒前
Cassie发布了新的文献求助10
14秒前
15秒前
感动新烟发布了新的文献求助10
15秒前
16秒前
医者发布了新的文献求助10
16秒前
Jon发布了新的文献求助10
16秒前
16秒前
18秒前
18秒前
19秒前
852应助123456采纳,获得10
19秒前
热心市民小红花应助乌禅采纳,获得10
20秒前
21秒前
22秒前
Rondab应助缥缈的机器猫采纳,获得10
23秒前
QL发布了新的文献求助10
23秒前
高分求助中
The Mother of All Tableaux Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 2400
Ophthalmic Equipment Market by Devices(surgical: vitreorentinal,IOLs,OVDs,contact lens,RGP lens,backflush,diagnostic&monitoring:OCT,actorefractor,keratometer,tonometer,ophthalmoscpe,OVD), End User,Buying Criteria-Global Forecast to2029 2000
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Cognitive Neuroscience: The Biology of the Mind (Sixth Edition) 1000
Optimal Transport: A Comprehensive Introduction to Modeling, Analysis, Simulation, Applications 800
Official Methods of Analysis of AOAC INTERNATIONAL 600
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3958968
求助须知:如何正确求助?哪些是违规求助? 3505216
关于积分的说明 11123184
捐赠科研通 3236828
什么是DOI,文献DOI怎么找? 1788949
邀请新用户注册赠送积分活动 871455
科研通“疑难数据库(出版商)”最低求助积分说明 802794