Adaptive Filtering and Feature Extraction of Ultrasonic Signal Based on FPGA
Liu Suzhen1,2, Wei Jian1,2,3, Zhang Chuang1,2, Jin Liang1,2, Yang Qingxin1
1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin 300130 China 2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province Hebei University of Technology Tianjin 300130 China 3. State Grid Hebei Electric Power Supply Co. Ltd Huanghua Power Supply Branch Cangzhou 061100 China
Abstract：Aiming at the nonlinear and non-stationary characteristics of electromagnetic ultrasonic characteristic signals, there are problems that traditional noise reduction components and features are difficult to extract. A data processing algorithm for adaptive filtering of electromagnetic ultrasonic signals and the empirical mode decomposition (EMD) method is proposed. Firstly, the stability evaluation of the ultrasonic signal is carried out. On this basis, the adaptive ultrasonic filtering is used to denoise the electromagnetic ultrasonic signal. The adaptive filtering integrated into the EMD is more sensitive to the unique frequency noise. The EMD is used to decompose the fluctuating time and frequency at different time scales. The information and the noise frequency components involved are used to realize the feature extraction. The reconstruction of the ultrasonic signal after EMD denoising can eliminate the frequency aliasing phenomenon, and realize the real-time noise reduction and feature extraction of the electromagnetic ultrasonic signal based on FPGA. The basis for further defect identification and defect assessment and portability has been laid. Finally, the experimental study on aluminum plates with microcracks and plastic damage was carried out, and the effectiveness of the method was verified. The method has the characteristics of high signal-to-noise ratio, real-time extraction of time-frequency information and less loss of effective information, and can effectively identify defects in the aluminum plate.
刘素贞, 魏建, 张闯, 金亮, 杨庆新. 基于FPGA的超声信号自适应滤波与特征提取[J]. 电工技术学报, 2020, 35(13): 2870-2878.
Liu Suzhen, Wei Jian, Zhang Chuang, Jin Liang, Yang Qingxin. Adaptive Filtering and Feature Extraction of Ultrasonic Signal Based on FPGA. Transactions of China Electrotechnical Society, 2020, 35(13): 2870-2878.
[1] 王淑娟, 康磊, 李智超, 等. 电磁超声换能器三维有限元分析及优化设计[J]. 中国电机工程学报, 2009, 29(30): 123-128. Wang Shujuan, Kang Lei, Li Zhichao, et al.3-D finite element analysis and optimum design of electromagnetic acoustic transducers[J]. Proceedings of the CSEE, 2009, 29(30): 123-128. [2] Ashigwuike E C, Ushie O J, Mackay R, et al.A study of the transduction mechanisms of electromagnetic acoustic transducers (EMATs) on pipe steel materials[J]. Sensors and Actuators A: Physical, 2015, 229: 154-165. [3] 孙文秀, 刘国强, 夏慧, 等. 非铁磁材料的电磁超声接收过程数值模拟及实验研究[J].电工技术学报, 2018, 33(19): 4443-4449. Sun Wenxiu, Liu Guoqiang, Xia Hui, et al.Numerical simulation and experimental study of electromagnetic acoustic transducer receiving process in nonferromagnetic material[J]. Transactions of China Electrotechnical Society, 2018, 33(19): 4443-4449. [4] 刘素贞, 李丽滨, 蔡智超, 等. 电磁超声检测系统中消除电磁干扰电路的设计[J]. 电工技术学报, 2016, 31(1): 80-84. Liu Suzhen, Li Libin, Cai Zhichao, et al.The Design for electromagnetic interference eliminating circuits in electromagnetic ultrasonic testing systems[J]. Transactions of China Electrotechnical Society, 2016, 31(1): 80-84. [5] Talebhaghighi Z, Bazzazi F, Sadr A.Design and simulation of ultrasonic denoising algorithm using wavelet transform and ICA[C]//The 2nd International Conference on Computer & Automation Engineering, Singapore, 2010, 1: 739-743. [6] 梁子千, 玄文博, 王婷, 等. 基于二维阻抗特征的管道环焊缝缺陷涡流检测[J]. 仪器仪表学报, 2017, 38(9): 54-61. Liang Ziqian, Xuan Wenbo, Wang Ting, et al.Eddy current NDT for the cracks of girth welds of pipes based on 2D impedance characteristics[J]. Chinese Journal of Scientific Instrument, 2017, 38(9): 54-61. [7] 汪开灿, 许霁, 翟国富. 基于电磁超声的铝板缺陷识别方法[J]. 浙江大学学报(工学版), 2014, 48(11): 2031-2038. Wang Kaican, Xu Ji, Zhai Guofu.Defect identification method for aluminum plate based on electromagnetic acoustic technique[J]. Journal of Zhejiang University: Engineering Science, 2014, 48(11): 2031-2038. [8] 南钢洋, 王启武, 张振振, 等. 基于激光超声方法的钢轨缺陷检测(英文)[J]. 红外与激光工程, 2017, 6(1): 132-137. Nan Gangyang, Wang Qiwu, Zhang Zhenzhen, et al.Rail steel flaw inspection based on laser ultrasonic method[J]. Infrared and Laser Engineering, 2017, 6(1): 132-137. [9] Lü Jingxiang, Liu Guoqiang.Noise reduction in magneto-acousto-electrical NDT using analytic wavelet thresholding[C]// 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC), 2017: 585-588. [10] 曹建树, 罗振兴, 姬保平. 应用微分算法处理特种管道测厚激光超声信号[J]. 光学精密工程, 2017, 25(5): 1197-1205. Cao Jianshu, Luo Zhenxing, Ji Baoping.Laser ultrasonic signal processing by differential aloorithm in special pipeline thickness measurement[J]. Optics and Precision Engineering, 2017, 25(5): 1197-1205. [11] 金亮, 寇晓斐, 郭富坤, 等. 基于电磁超声换能器的铁磁材料电磁声发射检测方法[J]. 电工技术学报, 2017, 32(18): 98-105. Jin Liang, Kou Xiaofei, Guo Fukun, et al.Electromagnetic acoustic emission detection method of ferromagnetic materials based on the EMAT[J]. Transactions of China Electrotechnical Society, 2017, 32(18): 98-105. [12] 张剑锋, 轩福贞, 项延训. 材料损伤的非线性超声评价研究进展[J]. 科学通报, 2016, 61(34): 1536-1550. Zhang Jianfeng, Xuan Fuzhen, Xiang Yanxun.Evaluation of material damage using nonlinear ultrasonic wave[J]. Chinese Science Bulletin, 2016, 61(34): 1536-1550. [13] 刘素贞, 李礼, 蔡智超, 等. 非线性电磁超声对铝合金拉伸变形评价研究[J]. 声学学报, 2017, 42(1): 60-66. Liu Suzhen, Li Li, Cai Zhichao, et al.Evaluation of the tensile deformation of aluminum alloy using EMAT for nonlinear ultrasonic detection[J]. Acta Acustica, 2017, 42(1): 60-66. [14] 王大为, 王召巴.一种强噪声背景下微弱超声信号提取方法研究[J].物理学报, 2018, 67(21): 65-77. Wang Dawei, Wang Zhaoba.Weak ultrasonic signal detection in strong noise[J]. Acta Physica Sinica, 2018, 67(21): 65-77. [15] Xu Hanhui, Xu Chunguang, Zhou Shiyuan.A new ultrasonic guided wave signal processing method for UNDE of laminated composite material[C]//1st International Conference on Mechanic Automation & Control Engineering, Wuhan, China, 2010: 2542-2545. [16] 刘永强, 杨世锡, 甘春标. 一种基于激光超声的薄层金属材料厚度检测方法研究[J]. 振动与冲击, 2018, 37(12): 147-152. Liu Yongqiang, Yang Shixi, Gan Chunbiao.Thickness measurement for thin metal material with the use of laser generated ultrasound[J]. Journal of Vibration and Shock, 2018, 37(12): 147-152. [17] Ma Ding, Shi Lihua, Cao Hongfu.Combination of wavelet transform with EMD to distinguish overlapped Lamb wave packets[C]//11th IEEE International Conference on Electronic Measurement & Instruments, Harbin, China, 2013, 1: 159-165. [18] Zhang Guangming, Harvey David M.Contemporary ultrasonic signal processing approaches for nondestructive evaluation of multilayered structures[J]. Nondestructive Testing & Evaluation, 2012, 27(1): 1-27. [19] Cui Jianguo, Wang Qingtian, Hua Jiaojiao, et al.Structure damage prediction of the composite material based on EMD[C]//Proceedings of the 33rd Control Conference, IEEE, Nanjing, China, 2014: 7784-7788. [20] 乐静, 郭俊杰, 朱虹. 基于EMD和Snakes模型信息融合的表面缺陷检测方法[J]. 仪器仪表学报, 2006, 27(12): 1664-1669. Le Jing, Guo Junjie, Zhu Hong.Surface defect defection method based on information fusion of EMD and Snakes model[J]. Chinese Journal of Scientific Instrument, 2006, 27(12): 1664-1669. [21] 吕敬祥, 刘国强. 磁声电无损检测及改进的EMD消噪方法[J].电工技术学报, 2018, 33(17): 3935-3942. Lü Jingxiang, Liu Guoqiang.Magneto-acousto-electrical NDT and improved EMD de-noising algorithm[J]. Transactions of China Electrotechnical Society, 2018, 33(17): 3935-3942.
2020, Vol. 35 
