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.
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.
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.
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.
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.
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.
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.