Abstract
Hypervelocity impact (HVI) vibration source identification and localization have found wide applications in many fields, such as manned spacecraft protection and machine tool collision damage detection and localization. In this paper, we study the synchrosqueezed transform (SST) algorithm and the texture color distribution (TCD) based HVI source identification and localization using impact images. The extracted SST and TCD image features are fused for HVI image representation. To achieve more accurate detection and localization, the optimal selective stitching features OSSST+TCD are obtained by correlating and evaluating the similarity between the sample label and each dimension of the features. Popular conventional classification and regression models are merged by voting and stacking to achieve the final detection and localization. To demonstrate the effectiveness of the proposed algorithm, the HVI data recorded from three kinds of high-speed bullet striking on an aluminum alloy plate is used for experimentation. The experimental results show that the proposed HVI identification and localization algorithm is more accurate than other algorithms. Finally, based on sensor distribution, an accurate four-circle centroid localization algorithm is developed for HVI source coordinate localization.
摘要
超高速碰撞(HVI)振动源识别与定位在载人航天器防护、机床碰撞损伤检测与定位等领域有着广泛应用。本文研究了基于同步压缩变换(SST)和纹理颜色分布(TCD)的冲击图像HVI源识别和定位算法。提出基于SST和TCD图像特征融合的HVI图像表示方法。为实现更精确的检测和定位, 通过关联和评估样本标签与特征维度之间的相似性, 获得最优选择性特征OSSST+TCD。将常用的分类和回归模型通过投票和堆叠融合, 实现最终的检测和定位。基于所采集的3种高速子弹撞击铝合金板产生的HVI数据, 验证了所提算法的有效性。实验结果表明本文提出的HVI识别与定位算法具有更高精准度。最后基于传感器分布, 提出一种精确的四圆质心定位算法用于HVI源坐标定位。
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Almeida LB, 1994. The fractional Fourier transform and time-frequency representations. IEEE Trans Signal Process, 42(11):3084–3091. https://doi.org/10.1109/78.330368
Anderson JLB, Schultz PH, 2006. Flow-field center migration during vertical and oblique impacts. Int J Impact Eng, 33(1–12):35–44. https://doi.org/10.1016/j.ijimpeng.2006.09.022
Auger F, Flandrin P, 1995. Improving the readability of time-frequency and time-scale representations by the reassignment method. IEEE Trans Signal Process, 43(5):1068–1089. https://doi.org/10.1109/78.382394
Bai X, Tao R, Liu LJ, et al., 2012. Autofocusing of SAR images using STFRFT-based preprocessing. Electron Lett, 48(25):1622–1624. https://doi.org/10.1049/el.2012.3169
Cao JW, Wang TL, Shang LM, et al., 2018. An intelligent propagation distance estimation algorithm based on fundamental frequency energy distribution for periodic vibration localization. J Franklin Inst, 355(4):1539–1558. https://doi.org/10.1016/j.jfranklin.2017.02.011
Cao JW, Zhang K, Yong HW, et al., 2019. Extreme learning machine with affine transformation inputs in an activation function. IEEE Trans Neur Netw Learn Syst, 30(7):2093–2107. https://doi.org/10.1109/TNNLS.2018.2877468
Cao JW, Dai HZ, Lei BY, et al., 2020. Maximum correntropy criterion-based hierarchical one-class classification. IEEE Trans Neur Netw Learn Syst, 7:1–7. https://doi.org/10.1109/TNNLS.2020.3015356
Chan HL, Huang HH, Lin JL, 2001. Time-frequency analysis of heart rate variability during transient segments. Ann Biomed Eng, 29(11):983–996. https://doi.org/10.1114/1.1415525
Cohen L, 2013. leID1. Time-frequency analysis: theory and applications. J Acoust Soc Am, 134(5):4002. https://doi.org/10.1121/1.4830599
Daubechies I, 1990. The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inform Theory, 36(5):961–1005. https://doi.org/10.1109/18.57199
Daubechies I, Maes S, 1996. A nonlinear squeezing of the continuous wavelet transform based on auditory nerve models. In: Aldroubi A, Unser M (Eds.), Wavelets in Medicine and Biology. CRC-Press, Boca Raton, USA.
Daubechies I, Lu JF, Wu HT, 2011. Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl Comput Harmon Anal, 30(2):243–261. https://doi.org/10.1016/j.acha.2010.08.002
Erickson AS, 2014. China’s space development history: a comparison of the rocket and satellite sectors. Acta Astronaut, 103:142–167. https://doi.org/10.1016/j.actaastro.2014.06.023
Flandrin P, Amin M, McLaughlin S, et al., 2013. Time-frequency analysis and applications. IEEE Signal Process Mag, 30(6):19–150. https://doi.org/10.1109/MSP.2013.2270229
Franco C, Guméry PY, Vuillerme N, et al., 2012. Synchrosqueezing to investigate cardio-respiratory interactions within simulated volumetric signals. Proc 20th European Signal Processing Conf, p.939–943.
Ghosh SK, Tripathy RK, Ponnalagu RN, et al., 2019. Automated detection of heart valve disorders from the PCG signal using time-frequency magnitude and phase features. IEEE Sens Lett, 3(12):7002604. https://doi.org/10.1109/LSENS.2019.2949170
Huang XG, Yin C, Huang J, et al., 2016. Hypervelocity impact of TiB2-based composites as front bumpers for space shield applications. Mater Des, 97:473–482. https://doi.org/10.1016/j.matdes.2016.02.126
Huang XG, Yin C, Ru HQ, et al., 2020. Hypervelocity impact damage behavior of B4C/Al composite for MMOD shielding application. Mater Des, 186:108323. https://doi.org/10.1016/j.matdes.2019.108323
Liou JC, Johnson NL, 2006. Risks in space from orbiting debris. Science, 311(5759):340–341. https://doi.org/10.1126/science.1121337
Liu LF, Cao JW, Huang XG, 2019. High speed collision localization based on surface vibration processing. Proc 12th Int Symp on Computational Intelligence and Design, p.35–38. https://doi.org/10.1109/ISCID.2019.10091
Mallat SG, 1989. Theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Patt Anal Mach Intell, 11(7):674–693. https://doi.org/10.1109/34.192463
Mamli S, Kalbkhani H, 2019. Gray-level co-occurrence matrix of Fourier synchro-squeezed transform for epileptic seizure detection. Biocybern Biomed Eng, 39(1):87–99. https://doi.org/10.1016/j.bbe.2018.10.006
Materka A, Strzelecki M, 1998. Texture Analysis Methods— a Review. COST B11 Report. University of Lodz, Brussels, Belgium.
Millan RM, von Steiger R, Ariel M, 2019. Small satellites for space science: a cospar scientific roadmap. Adv Space Res, 64(8):1466–1517. https://doi.org/10.1016/j.asr.2019.07.035
Mirzapour F, Ghassemian H, 2013. Using GLCM and Gabor filters for classification of PAN images. 21st Iranian Conf on Electrical Engineering, p.1–6.
Monti A, Medigue C, Mangin L, 2002. Instantaneous parameter estimation in cardiovascular time series by harmonic and time-frequency analysis. IEEE Trans Biomed Eng, 49(12):1547–1556. https://doi.org/10.1109/TBME.2002.805478
Önsay T, Haddow AG, 1993. Comparison of STFT, Gabor, and wavelet transforms in transient vibration analysis of mechanical systems. J Acoust Soc Am, 93(4):2290. https://doi.org/10.1121/1.406528
Pierazzo E, Melosh HJ, 2000. Hydrocode modeling of oblique impacts: the fate of the projectile. Meteorit Planet Sci, 35(1):117–130. https://doi.org/10.1111/j.1945-5100.2000.tb01979.x
Qian SE, Chen DP, 1999. Joint time-frequency analysis. IEEE Signal Process Mag, 16(2):52–67. https://doi.org/10.1109/79.752051
Shang LM, Cao JW, Wang JZ, et al., 2016. Fundamental frequency energy distribution of periodic vibrations and their relation to distance. Proc IEEE 13th Int Conf on Signal Processing, p.96–101. https://doi.org/10.1109/ICSP.2016.7877804
Stankovic L, Stankovic S, Dakovic M, 2014. From the STFT to the Wigner distribution. IEEE Signal Process Mag, 31(3):163–174. https://doi.org/10.1109/MSP.2014.2301791
Tao R, Li YL, Wang Y, 2010. Short-time fractional Fourier transform and its applications. IEEE Trans Signal Process, 58(5):2568–2580. https://doi.org/10.1109/TSP.2009.2028095
Thakur G, Brevdo E, Fučkar NS, et al., 2013. The synchrosqueezing algorithm for time-varying spectral analysis: robustness properties and new paleoclimate applications. Signal Process, 93(5):1079–1094. https://doi.org/10.1016/j.sigpro.2012.11.029
Torrence C, Compo G, 1998. A practical guide to wavelet analysis. Bull Amer Meteorol Soc, 79(79):61–78.
Wang TL, Cao JW, Lai XP, et al., 2020. Hierarchical one-class classifier with within-class scatter-based autoencoders. IEEE Trans Neur Netw Learn Syst, 32(8):3770–3776. https://doi.org/10.1109/TNNLS.2020.3015860
Wilson EK, 2019. Space tourism moves closer to lift off. Engineering, 5(5):819–821. https://doi.org/10.1016/j.eng.2019.08.006
Witze A, 2018. The quest to conquer Earth’s space junk problem. Nature, 561(7721):24–26. https://doi.org/10.1038/D41586-018-06170-1
Yang MQ, Kpalma K, Ronsin J, 2008. A survey of shape feature extraction techniques. Patt Recogn, 15(7):43–90.
Yin C, Xue T, Huang XG, et al., 2019. Research on damages evaluation method with multi-objective feature extraction optimization scheme for M/OD impact risk assessment. IEEE Access, 7:98530–98545. https://doi.org/10.1109/ACCESS.2019.2930114
Zhou ZH, 2016. Machine Learning. Tsinghua University Press, Beijing, China (in Chinese).
Author information
Authors and Affiliations
Corresponding author
Additional information
Project supported by the National Natural Science Foundation of China (Nos. U1909209 and 61503104), the Open Foundation of Hypervelocity Impact Research Center of China Aerodynamics Research and Development Center, and the Research Start-up Funding, China (No. 2019RC020)
Contributors
Jiao BAO and Lifu LIU completed the experiments, processed the data, and drafted the paper. Jiuwen CAO designed the research, organized the paper, and revised and finalized the paper.
Compliance with ethics guidelines
Jiao BAO, Lifu LIU, and Jiuwen CAO declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Bao, J., Liu, L. & Cao, J. Vibration-based hypervelocity impact identification and localization. Front Inform Technol Electron Eng 23, 515–529 (2022). https://doi.org/10.1631/FITEE.2000483
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.2000483
Key words
- Ensemble learning
- Synchrosqueezied transform
- Gray-level co-occurrence matrix
- Image entropy
- Distance estimation