E-mail Alert
RSS
-
摘要:
针对激光测振仪中的自适应滤波问题，本文采用最小均方误差前向预测器，仿真分析了振动测量信号的峰值、频率、滤波器阶数和步长系数等参数对最小均方误差前向预测器滤波性能的影响，并搭建实验系统进行了验证。研究结果可以作为最小均方误差前向预测器参数选择的理论依据，为设计适用于激光测振仪自适应滤波器提供技术手段。
Abstract:Aiming at adaptive filtering in the laser vibrograph, we simulated and analyzed the parameters of the Least Mean Square forward predictors, such as the peak value and frequency of the vibration measurement signal, the order of the filter and the step size coefficient on the filtering performance. Then we built an experimental system for verification, and an experimental system was built to verify it. The research results can be used as the theoretical basis for the parameter selection of the minimum mean square error forward predictor and provides a technical means for designing an adaptive filter suitable for laser vibrometer.
-
Key words:
- vibration measurement /
- adaptive filtering /
- LMS /
- forward predictor
-
Overview: Laser vibration measurement technology has made great progress in the past decades, and there is still a higher demand in the measurement accuracy and measurement range. Due to the large amount of noise in the measurement process of the laser vibrometer, which leads to a decrease of the vibration measurement precision of the laser vimeter, the filtering of the vibration measurement signal is the key to improving the precision of the laser vimeter. Traditional filters such as the FIR and IIR are time-invariant filters, whose parameters are fixed and invariable. The frequency range of the input signal is required to be known during design, and the filtering performance is inversely proportional to the bandwidth. An adaptive filter is a time-variant filter, it does not need to predict the statistical properties of interference noise, can filter in successive iteration processes of the working state of convergence to adaptively based on the optimal solution under the certain standards, such as minimum mean square error (MMSE) and least-squares criterion, are effective for broadband and narrowband noise suppression. The Least Mean Square (LMS) forward predictor is a kind of the classical adaptive filter, which is based on the MMSE criterion and uses the stochastic gradient descent method to approach the optimal solution under the MMSE criterion by iteration. It has the advantages of simple structure and good robustness. Aiming at the adaptive filtering problem in the laser vibrometer, the Least Mean Square (LMS) forward predictor was used in this paper, and the vibration measurement signal model was established. We simulated and analyzed the parameters of the LMS forward predictor, such as the peak value and frequency of the vibration measurement signal, the order of the filter and the step size coefficient on the filtering performance, and built an experimental system for verification. Simulation and experiments show that the LMS forward predictor can be used as a way to realize adaptive filtering of laser vibrometers, which is suitable for vibration velocity signal filtering in applications such as building vibration detection, mechanical vibration measurement, and material surface micro-damage detection. The filtering effect and convergence speed of the LMS forward predictor are affected by the peak value, order, and step coefficient of the input signal. The filter parameters can be selected and designed according to the requirements of the system for the minimum filtering signal-to-noise ratio and vibration velocity measurement range. This paper provides a theoretical basis for the parameter selection of the LMS forward predictor and provides a technical means for designing an adaptive filter suitable for a laser vibrometer.
-
-
图 9 振动速度信号自适应滤波数据处理流程框图
Figure 9. Block diagram of adaptive filtering data processing flow for vibration velocity signal
图 11 滤波前后波形和频谱对比。
Figure 11. Waveform and spectrum comparison before and after filtering.
图 12 输入信号频率与滤波信噪比关系的实验结果
Figure 12. Experiment on the relation between input signal frequency and filter SNR
图 13 输入信号峰值与滤波信噪比关系的实验结果
Figure 13. Experiment on the relationship between input signal peak value and filter SNR
-
[1] Fu X J, Peng S L, Zhang C Y, et al. Normalized radar cross section measurement for space objects[C]//Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, Hefei, 2016: 2372–2375.
[2] Dahak M, Touat N, Benseddiq N. On the classification of normalized natural frequencies for damage detection in cantilever beam[J]. J Sound Vib, 2017, 402: 70−84. doi: 10.1016/j.jsv.2017.05.007
[3] He W Y, Ren W X, Zuo X H. Mass-normalized mode shape identification method for bridge structures using parking vehicle-induced frequency change[J]. Struct Control Health Monit, 2018, 25(6): e2174. doi: 10.1002/stc.2174
[4] Ibarra-Castanedo C, Maldague X P V. Pulsed phase thermography inversion procedure using normalized parameters to account for defect size variations[J]. Proc SPIE, 2005, 5782: 334−341. doi: 10.1117/12.596602
[5] 张展. 耳语音信号处理研究及其在激光侦听中的应用[D]. 南京: 南京航空航天大学, 2017: 1–9.
Zhan Z. Research on whisper speech signal processing and its application in laser interception[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2017: 1–9.
[6] 周健, 冯庆奇, 马曙光, 等. 参考光束型激光多普勒测速仪的误差分析[J]. 强激光与粒子束, 2010, 22(11): 2581−2587. doi: 10.3788/HPLPB20102211.2581
Zhou J, Feng Q Q, Ma S G, et al. Error analysis of reference-beam laser Doppler velocimeter[J]. High Power Laser Part Beams, 2010, 22(11): 2581−2587. doi: 10.3788/HPLPB20102211.2581
[7] 孔新新, 张文喜, 才啟胜, 等. 基于多光束混合外差干涉的相位增强技术研究[J]. 物理学报, 2020, 69(19): 190601. doi: 10.7498/aps.69.20200281
Kong X X, Zhang W X, Cai Q S, et al. Multi beam hybrid heterodyne interferometry based phase enhancement technology[J]. Acta Phys Sin, 2020, 69(19): 190601. doi: 10.7498/aps.69.20200281
[8] 晏春回. 激光相干测振信号处理技术研究[D]. 长春: 中国科学院长春光学精密机械与物理研究所, 2019: 40–46.
Yan C H. Research on signal processing technology of laser coherent vibration measurement[D]. Changchun: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, 2019: 40–46.
[9] Saho K, Masugi M. Performance analysis of α-β-γ tracking filters using position and velocity measurements[J]. EURASIP J Adv Signal Process, 2015, 2015: 35. doi: 10.1186/s13634-015-0220-3
[10] Shao H S, Lee W R, You K H. Robust adaptive Kalman filtering for nonlinearity compensation in laser interferometer[C]//Proceedings of the 2010 the 2nd International Conference on Industrial Mechatronics and Automation, Wuhan, 2010: 206–209.
[11] 张达, 孙圣军. 基于小波滤波的激光多普勒信号研究[J]. 机械制造, 2017, 55(11): 39−42,45. doi: 10.3969/j.issn.1000-4998.2017.11.012
Zhang D, Sun S J. Research on laser Doppler signal based on wavelet filtration[J]. Machinery, 2017, 55(11): 39−42,45. doi: 10.3969/j.issn.1000-4998.2017.11.012
[12] 吴俊, 管鲁阳, 鲍明, 等. 基于多尺度一维卷积神经网络的光纤振动事件识别[J]. 光电工程, 2019, 46(5): 180493.
Wu J, Guan L Y, Bao M, et al. Vibration events recognition of optical fiber based on multi-scale 1-D CNN[J]. Opto-Electron Eng, 2019, 46(5): 180493.
[13] 张永康, 尚盈, 王晨, 等. 分布式光纤入侵信号检测与识别[J]. 光电工程, 2021, 48(3): 200254.
Zhang Y K, Shang Y, Wang C, et al. Detection and recognition of distributed optical fiber intrusion signal[J]. Opto-Electron Eng, 2021, 48(3): 200254.
[14] 刘帆, 金世龙, 周健. 自适应滤波技术在激光多普勒测速仪中的应用[J]. 应用光学, 2012, 33(3): 570−574.
Liu F, Jin S L, Zhou J. Application of least mean square adaptive filter technology in laser Doppler velocimeter[J]. J Appl Opt, 2012, 33(3): 570−574.
[15] 徐新龙. 自适应滤波算法及其应用研究[D]. 上海: 复旦大学, 2014: 1–7.
Xu X L. Research on adaptive filtering algorithms and application[D]. Shanghai: Fudan University, 2014: 1–7.
[16] 樊昌信, 张甫翊, 徐炳祥. 通信原理[M]. 5版. 北京: 国防工业出版社, 2001: 21–27.
Fan C X, Zhang F Y, Xu B X. Principles of Communications[M]. 5th ed. Beijing: National Defence Industry Press, 2001:21−27.
[17] Haykin S. 自适应滤波器原理[M]. 郑宝玉, 译. 2版. 北京: 电子工业出版社, 2016: 150–232.
Haykin S. Adaptive Filter Theory[M]. Zheng B Y, trans. 2nd ed. Beijing: Publishing House of Electronics Industry, 2016: 150–232.
-
下载:
点击扫一扫
图(14)
计量
- 文章访问数: 3997
- PDF下载数: 653
- 施引文献: 0
