面向水下焊接电信号的粒子群优化小波软阈值去噪法研究

李康; 吕卫文; 夏卫生

doi:10.7512/j.issn.1001-2303.2023.12.06

重点关注 | 浏览量 : 0 下载量: 4 CSCD: 0

PDF
导出
分享
收藏
专辑

面向水下焊接电信号的粒子群优化小波软阈值去噪法研究
De-Noising for Underwater Welding Electrical Signals by PSO Wavelet Soft Threshold Method
2023年53卷第12期页码：40-45
DOI： 10.7512/j.issn.1001-2303.2023.12.06

扫描看全文

李康，吕卫文，夏卫生.面向水下焊接电信号的粒子群优化小波软阈值去噪法研究［J］.电焊机，2023，53（12）：40-45.

LI Kang, LV Weiwen, XIA Weisheng.De-Noising for Underwater Welding Electrical Signals by PSO Wavelet Soft Threshold Method[J].Electric Welding Machine, 2023, 53(12): 40-45.
李康，吕卫文，夏卫生.面向水下焊接电信号的粒子群优化小波软阈值去噪法研究［J］.电焊机，2023，53（12）：40-45. DOI： 10.7512/j.issn.1001-2303.2023.12.06.

LI Kang, LV Weiwen, XIA Weisheng.De-Noising for Underwater Welding Electrical Signals by PSO Wavelet Soft Threshold Method[J].Electric Welding Machine, 2023, 53(12): 40-45. DOI： 10.7512/j.issn.1001-2303.2023.12.06.

摘要

为降低噪声干扰以及提高检测精度，提出一种针对水下焊接电信号的基于粒子群优化的小波软阈值去噪方法。通过对信号进行小波分解，以信噪比作为评价指标，不断迭代进行全局搜索最优阈值从而完成信号去噪，并分别进行仿真及实测水下焊接电信号的去噪实验。结果表明，提出的算法具有信号信噪比更高、均方根误差更小、能有效保留信号的细节信息等优点，有利于水下焊接电信号特征的提取，从而为水下焊接质量在线监测提供基础。

Abstract

In order to reduce noise interference and improve detection accuracy, a wavelet soft threshold denoising method based on particle swarm optimization (PSO) for electrical signals of underwater welding was presented. Through wavelet decomposition of the signals, the signal-to-noise ratio was selected as the evaluation index, and the optimal threshold value was searched for the global iteratively to complete the signal denoising. The denoising experiments for the simulated and the measured electrical signals of underwater welding process were carried out. It is proved that the signal-to-noise ratio was improved, the root mean square error was reduced, and the detailed information of the signals can be effectively preserved after denoising by this algorithm. It is suitable for the extraction of electrical signals features of underwater welding, laying the foundation for the online monitoring of underwater welding.

关键词

水下焊接粒子群优化算法小波软阈值电信号去噪

Keywords

underwater weldingparticle swarm optimization algorithmwavelet soft thresholdelectrical signal denoising

references

Tomkow J， Rogalski G， Fydrych D， et al. Improvement of S355G10+N steel weldability in water environment by Temper Bead Welding［J］. Journal of Materials Processing Technology， 2018， 262： 372-381.

杜永鹏. 水下湿法焊接过程稳定性及熔滴过渡控制研究［D］. 黑龙江：哈尔滨工业大学， 2019.

DU Y P. Research on underwater wet welding stability and metal transfer control［D］. Heilongjiang： Harbin Institute of Technology， 2019.

Shi Y， Hu Y， Yi Y， et al. Porosity and Microstructure of Underwater Wet FCAW of Duplex Stainless Steel［J］.Metallography，Microstructure，and Analysis，2017，6（5）：383-389.

王硕，王昕，蒋国臻，等. 增强灰狼自适应阈值去噪法在变压器套管引线超声检测中的应用［J］. 传感技术学报， 2019， 32（10）： 1595-1602.

WANG S， WANG X， JIANG G Z， et al. Application of enhanced grey wolf adaptive threshold denoising method in ultrasound detection of transformer bushing lead［J］. Chinese Journal of Sensors and Actuators，2019， 32（10）： 1595-1602.

米翰宁，王昕，任广振，等. 自适应小波阈值去噪算法用于局部放电白噪声去噪［J］. 高压电器， 2021， 57（06）： 94-101.

MI H N， WANG X， REN G Z， et al. De-noising for white noise in partial discharge signals by adaptive wavelet threshold estimation［J］. High Voltage Apparatus， 2021， 57（6）： 94-101.

李清泉，秦冰阳，司雯，等. 混合粒子群优化小波自适应阈值估计算法及用于局部放电去噪［J］. 高电压技术， 2017， 43（05）： 1485-1492.

LI Q Q， QIN B Y， SI W， et al. Estimation algorithm for adaptive threshold of hybrid particle swarm optimization wavelet and its application in partial discharge signals de-noising［J］.High Voltage Engineering，2017，43（5）： 1485-1492.

Liu Z P， Zhang L， Carrasco J. Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method［J］. Renew Energy， 2020， 146： 99-110.

吴飞，马晨浩，程坤，等. 基于改进阈值的振动信号小波去噪方法研究［J］. 合肥工业大学学报（自然科学版）， 2022， 45（07）： 873-900.

WU F，MA C H，CHENG K，et al. Study on wavelet denoising method of vibration signal based on improved threshold［J］. Journal of Hefei University of Technology （Natural Science），2022，45（07）：873-900.

Xie Z J， Song B Y， Zhang Y， et al. Application of an Improved Wavelet Threshold Denoising Method for Vibration Signal Processing［J］. Advanced Materials Research， 2014， 889： 799-806.

龙虹毓，张晓勇，胡晓锐，等. 蚁群优化小波阈值算法用于变电设备状态信号提取［J］. 电工技术学报， 2015， 30（12）： 422-428.

LONG H Y， ZHANG X Y， HU X R， et al. Extraction of condition signals of electrical plants by ACO wavelet threshold estimation［J］. Transactions of China Electrotechnical Society， 2015， 30（12）： 422-428.

Baldazzi G， Solinas G， Del Valle J， et al. Systematic analysis of wavelet denoising methods for neural signal processing［J］. Journal of Neural Engineering， 2020， 17（6）： 18-37.

律方成，张瑜，董蒙，等. 基于复合评价指标的金属微粒声发射信号最优小波去噪及其特征提取［J］. 高压电器， 2017， 53（11）： 1-8.

LV F C， ZHANG Y， DONG M， et al. Optimal wavelet denoising and feature extraction of acoustic emission signal caused by the metal particles by using composite evaluation index［J］. High Voltage Apparatus， 2017， 53（11）： 1-8.

Eberhart R C， Kennedy J. A new optimizer using particle swarm theory［J］. MHS'95 Proceedings of the Sixth International Symposium on Micro Machine and Human Science， 1995： 39-43.

Chang Y P， Koh C N. A PSO method with nonlinear time-varying evolution based on neural network for design of optimal harmonic filters［J］. Expert System with Applications， 2009， 36（3）： 6809-6816.

编辑部网址：http：//www.71dhj.comhttp://www.71dhj.com

文章被引用时，请邮件提醒。

提交

核电厂水下TIG焊炬设计及焊接工艺研究