Research on De-noising Method of Output Displacement Signal for Giant Magnetostrictive Actuator
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摘要: 为了去除超磁致伸缩致动器输出位移信号夹杂的噪声,运用形态分量分析理论对输出位移信号进行降噪处理。针对形态分量分析算法中阈值处理的潜在缺点,通过构造模糊阈值函数,提出了模糊形态分量分析算法。仿真与实验结果表明:从降噪后信号对比看,模糊形态分量分析算法得到的致动器输出位移信号噪声基本被去除干净;从仿真评价指标比较,模糊形态分量分析算法的信噪比最高、均方根误差最小、相似系数最大。因此模糊形态分量分析算法显示出了在超磁致伸缩致动器输出位移信号降噪方面有着独特优势。Abstract: In order to remove or suppress the noise in the output displacement signal of the giant magnetostrictive actuator, the output displacement signal was processed and denoised based on the morphological component analysis theory. Because of the potential shortcomings of threshold processing in morphological component analysis algorithm, this paper proposes the fuzzy morphological component analysis algorithm by constructing fuzzy threshold function. Simulation and experimental results show that, from the comparison of the signals after de-noising, the noise of the output displacement signal is basically removed by the fuzzy morphological component analysis. From the comparison of simulation assessment indexes, the fuzzy morphological component analysis algorithm has the highest signal-to-noise ratio, the smallest mean square error and the largest similarity coefficient. Therefore, the fuzzy morphological component analysis algorithm shows a unique advantage in the de-noising of the output displacement signal for the giant magnetostrictive actuator.
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表 1 仿真参数
参数名 数值及单位 串联电阻R1 6.271 Ω 串联电感L 7.693 8×10-3 mH 并联电阻R2 563.279 8 Ω 磁阻系数CH 0.4 线圈匝数N 800 棒长lGM 40 mm 畴壁相互作用系数α 0.001 非磁滞形状系数a 5 000 A/m 饱和磁化强度Ms 800 kA/m 饱和应变λs 6.3×10-4 总质量m 45 g 总阻尼ζ 200 负载刚度kl 560 N/m 弹性模量E 3×1010 N/m2 超磁致伸缩棒直径 5 mm 表 2 降噪效果对比
名称 小波硬阈值降噪 MCA硬阈值降噪 FMCA降噪 电流/A 2 10 2 10 2 10 SNR/dB 20.73 23.77 33.05 32.93 35.19 34.73 MSE 0.500 6 1.460 3 0.121 7 0.51 0.095 1 0.414 4 NCC 0.995 8 0.997 9 0.999 8 0.999 7 0.999 9 0.999 8 -
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