Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Structural instantaneous frequency extraction based on improved multi-synchrosqueezing generalized S-transform

  • Yuan, Ping-Ping (School of Materials Science and Engineering, Jiangsu University of Science and Technology) ;
  • Cheng, Xue-Li (School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology) ;
  • Wang, Hang-Hang (School of Civil Engineering and Architecture, Jiangsu University of Science and Technology) ;
  • Zhang, Jian (School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology) ;
  • Shen, Zhong-Xiang (School of Civil Engineering and Architecture, Jiangsu University of Science and Technology) ;
  • Ren, Wei-Xin (College of Civil and Transportation Engineering, Shenzhen University)
  • Received : 2021.02.09
  • Accepted : 2021.08.03
  • Published : 2021.11.25
⟨ Previous Next ⟩

Abstract

A new method is proposed to improve the accuracy of structural instantaneous frequency (IF) extraction. The proposed method combines a new form of improved generalized S-transform (IGST) and a multi-synchrosqueezing operation. The parameters selection of the window function in IGST is derived through the concentration measure (CM) principle. Then, the multi-synchrosqueezing algorithm is employed to improve energy aggregation of time-frequency analysis (TFA). To verify the effectiveness and accuracy of the proposed improved multi-synchrosqueezing generalized S-transform (IMSSGST), a frequency-modulated multi-component signal is investigated. For structural IF extraction, a two-story shear frame and a three-story steel frame structure are introduced. Furthermore, the IF identification of a seven-story RC shear wall structure is conducted to verified the practicability in actual engineering. Numerical simulation and experimental results show that the proposed method can effectively improve the energy aggregation of TFA and effectively improve the accuracy of IF identification.

Keywords

Acknowledgement

Financial support to complete this study is provided in part by the National Natural Science Foundation of China (Grant No. 51979130), Natural Science Research of Jiangsu Higher Education Institutions of China (Grant No. 20KJB560016), and Foundation of Jiangsu University of Science and Technology (Grant No. 1122931804). The results and opinions expressed in this paper are those of the authors only and they don't necessarily represent those of the sponsors.

References

  1. Assous, S. and Boashash, B. (2012), "Evaluation of the modified S-transform for time-frequency synchrony analysis and source localization", Eurasip J. Adv. Signal Process, 2012, 49. https://doi.org/10.1186/1687-6180-2012-49
  2. Behera, R., Meignen, S. and Oberlin, T. (2018), "Theoretical analysis of second-order synchrosqueezing transform", Appl. Comput. Harmon. Anal., 45(2), 379-404. https://doi.org/10.1016/j.acha.2016.11.001
  3. Cao, H., Xi, S., Chen, X. and Wang, S. (2016), "Zoom synchrosqueezing transform and iterative demodulation: Methods with application", Mech. Syst. Signal Pr., 72-73, 695-711. https://doi.org/10.1016/j.ymssp.2015.11.030
  4. Daubechies, I., Lu, J.F. and Wu, H.T. (2011), "Synchrosqueezed wavelet transforms: An empirical mode decomposition decomposition-like tool", Appl. Comput. Harmon. A., 30(2), 243-261. https://doi.org/10.1016/j.acha.2010.08.002
  5. Djurovic, I., Sejdic, E. and Jiang, J. (2008), "Frequency-based window width optimization for S-transform", AEU-Int. J. Electron. C., 62, 245-250. https://doi.org/10.1016/j.aeue.2007.03.014
  6. Feng, Z., Chen, X. and Liang, M. (2015), "Iterative generalized synchrosqueezing transform for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions", Mech. Syst. Signal Pr., 52, 360-375. https://doi.org/10.1016/j.ymssp.2014.07.009
  7. Guo, S.X. and Pei, Q. (2017), "Application of short-time Fourier transform to high-rise frame structural-health monitoring based on change of inherent frequency over time", J. Chongqing Univ. (English Edition),16(01), 1-10.
  8. Hou, Z., Hera, A. and Shinde, A. (2010), "Wavelet-based structural health monitoring of earthquake excited structures", Comput-Aided Civ. Inf., 21(4), 268-279. https://doi.org/10.1111/j.1467-8667.2006.00434.x
  9. Kang, J.X. (2018), "Research on improved synchroextracting transform and its application in seismic signal processing", ChengDu University of Technology.
  10. Kijewski, T. and Kareem, A. (2010), "Wavelet transforms for system identification in civil engineering", Comput.-Aid. Civ. Inf., 18(5), 339-355. https://doi.org/10.1111/1467-8667.t01-1-00312
  11. Le, T.P. and Argoul, P. (2015), "Distinction between harmonic and structural components in ambient excitation tests using the time-frequency domain decomposition technique", Mech. Syst. Signal Pr., 52-53, 29-45. https://doi.org/10.1016/j.ymssp.2014.07.008
  12. Lei, Y., Lin, J., He, Z., and Zuo, M.J. (2013), "A review on empirical mode decomposition in fault diagnosis of rotating machinery", Mech. Syst. Signal Pr., 35, 108-126. https://doi.org/10.1016/j.ymssp.2012.09.015
  13. Li, C. and Liang, M. (2012), "A generalized synchrosqueezing transform for enhancing signal time time-frequency representation", Signal Process., 92(9), 2264-2274. https://doi.org/10.1016/j.sigpro.2012.02.019
  14. Li, Z., Gao, J., Wang, Z., Liu, N. and Yang, Y. (2020), "Time-synchroextracting general Chirplet transform for seismic time-frequency analysis", IEEE Trans. Geosci. Remote Sens., 58(12), 8626-8636. https://doi.org/10.1109/TGRS.2020.2989403
  15. Liu, J.L., Wang, Z.C., Ren, W.X. and Li, X.X. (2015), "Structural time-varying damage detection using synchrosqueezing wavelet transform", Smart Struct. Syst., Int. J., 15(1), 119-133. https://doi.org/10.12989/sss.2015.15.1.119
  16. Liu, N., Gao, J., Zhang, B., Li, F. and Wang, Q. (2017a), "Time-frequency analysis of seismic data using a three parameters S transform", IEEE Geosci. Remote S., 15(1), 142-146. https://doi.org/10.1109/LGRS.2017.2778045
  17. Liu, N., Gao, J., Zhang, Z., Jiang, X. and Lv, Q. (2017b), "High-resolution characterization of geologic structures using the synchrosqueezing transform", Interpret.-J. Sub., 5(1), 75-85. https://doi.org/10.1190/INT-2016-0006.1
  18. Liu, J.L., Wei, X.J., Qiu, R.H., Zheng, J.Y., Zhu, Y.J. and Irwanda, L. (2018a), "Instantaneous frequency extraction in time-varying structures using a maximum gradient method", Smart Struct. Syst., Int. J., 22(3), 359-368. https://doi.org/10.12989/sss.2018.22.3.359
  19. Liu, N., Gao, J., Jiang, X., Zhang, Z. and Wang, P. (2018b), "Seismic instantaneous frequency extraction based on the SST-MAW", J. Geophys. Eng., 15(3), 995-1007. https://doi.org/10.1088/1742-2140/aa8cb6
  20. Liu, N., Gao, J., Zhang, B., Wang, Q. and Jiang, X. (2019), "Self-adaptive generalized S-transform and its application in seismic time-frequency analysis", IEEE Geosci. Remote S., 57(10), 7849-7859. https://doi.org/100.1109/TGRS.2019.2916792 https://doi.org/10.1109/TGRS.2019.2916792
  21. Moukadem, A., Bouguila, Z., Ould-Abdeslam, D. and Dieterlen, A. (2015), "A new optimized Stockwell transform applied on synthetic and real non-stationary signals", Digit. Signal Process., 46, 226-238. https://doi.org/10.1016/j.dsp.2015.07.003
  22. Ni, S.H., Yang, Y.Z. and Tsai, P.H. (2017), "Evaluation of pile defects using complex continuous wavelet transform analysis", NDT&E Int., 87, 50-59. https://doi.org/10.1016/j.ndteint.2017.01.007
  23. Oberlin, T., Meignen, S. and Perrier, V. (2015), "Second-order synchrosqueezing transform or invertible reassignment? Towards ideal time time-frequency representations", IEEE T. Signal Process., 63(5), 1335-1344. https://doi.org/10.1109/TSP.2015.2391077
  24. Panagiotou, M., Restrepo, Jose, I. and Conte, J.P. (2011), "Shake-table test of a full-scale 7-story building slice. Phase I: Rectangular wall", J. Struct. Eng., 137(6), 691-704. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000332
  25. Patil, D.O. and Hamde, S.T. (2021), "Automated detection of brain tumor disease using empirical wavelet transform based LBP variants and ant-lion optimization", Multimed. Tools Appl., 80, 17955-17982. https://doi.org/10.1007/s11042-020-10434-2
  26. Pham, D.H. and Meignen, S. (2017), "High-order synchrosqueezing transform for multicomponent signals analysis-with an application to gravitational gravitational-wave signal", IEEE T. Signal Process., 65(12), 3168-3177. https://doi.org/10.1109/TSP.2017.2686355
  27. Pinnergar, C.R. and Mansinha, L. (2003), "The S-transform with windows of arbitrary and varying shape", Geophysics, 68, 381-385. https://doi.org/10.1190/1.1543223
  28. Sanchez, W.D., Brito, J.V.D. and Avila, S.M. (2020), "Structural health monitoring using synchrosqueezed wavelet transform on IASC-ASCE benchmark phase I", Int. J. Struct. Stab. Dyn., 20(12), 2050138. https://doi.org/10.1142/S0219455420501382
  29. Stankovic, L. (2001), "A measure of some time-frequency distributions concentration", Signal Process., 81(3), 621-631. https://doi.org/10.1016/S0165-1684(00)00236-X
  30. Thakur, G. and Wu, H.T. (2011), "Synchrosqueezing-based recovery of instantaneous frequency from nonuniform samples", SIAM J. Math. Anal., 43(5), 2078-2095. https://doi.org/10.1137/100798818
  31. Wang, Z.C., Ren, W.X. and Chen, G.D. (2018), "Time-frequency analysis and applications in time-varying/nonlinear structural systems: A state-of-the-art review", Adv. Struct. Eng., 21(10), 1562-1584. https://doi.org/10.1177/1369433217751969
  32. Xu, X.Z., Zhang, Z.Y., Hua, H.X. and Chen, Z.N. (2003), "Identification of time-varying modal parameters by a linear time-frequency method", J. Vib. Eng., 3, 104-108.
  33. Yu, G., Yu, M. and Xu, C. (2017), "Synchroextracting transform", IEEE T. Ind. Electron., 64(10), 8042-8054. https://doi.org/10.1109/TIE.2017.2696503
  34. Zhang, Z.Y., Liu, Y.X. and Li, X.Y. (2018), "Extracting instantaneous attributes of seismic signals with synchrosqueezing wavelet transform", Comput. Measur. Control, 26(10), 260-263.
  35. Zhang, W.H., Hu, M.H., Jiang, Z.N. and Feng, K. (2020), "Order tracking method for gas turbine combined with synchroextracting transform and Vold-Kalman filtering", J. Mech. Electr. Eng., 37(09), 1104-1108.
  36. Zheng, J., Pan, H., Yang, S. and Cheng, J. (2017), "Adaptive parameterless empirical wavelet transform based time time-frequency analysis method and its application to rotor rubbing fault diagnosis", Signal Process Process., 130, 305-314. https://doi.org/10.1016/j.sigpro.2016.07.023
  37. Zidelmal, Z., Hamil, H., Moukadem, A., Amirou, A. and Ould-Abdeslam, D. (2017), "S-transform based on compact support kernel", Digit. Signal Process., 62, 137-149. https://doi.org/10.1016/j.dsp.2016.11.008