DOI QR코드

DOI QR Code

A new time-frequency analysis and structural instantaneous frequency extraction method based on modified spline-kernelled chirplet transform

  • Dong-Yan Xue (School of Civil Engineering and Architecture, Jiangsu University of Science and Technology) ;
  • Ping-Ping Yuan (School of Civil Engineering and Architecture, Jiangsu University of Science and Technology) ;
  • Zhou-Jie Zhao (School of Civil Engineering and Architecture, Jiangsu University of Science and Technology) ;
  • Wei-Xin Ren (College of Civil and Transportation Engineering, Shenzhen University)
  • 투고 : 2023.05.31
  • 심사 : 2024.06.12
  • 발행 : 2024.06.25

초록

To improve the accuracy of time-frequency analysis (TFA) and instantaneous frequency (IF) extraction of structural dynamic response signals, this paper improves the spline-kernelled chirplet transform, and a new form of modified spline-kernelled chirplet transform (MSCT) based on revised Gaussian window function and energy concentration principle is put forward. The effectiveness of the proposed method is verified by numerical examples of single-component signal, multicomponent signal, single-degree-of-freedom Duffing nonlinear system and two-layer shear frame structure model. Then, a time-varying cable test is designed to collect the acceleration response signals under linear changing tension, and the IF extraction of these signals is performed by using MSCT, which further verifies the effectiveness and accuracy of this method. Through numerical simulation and experimental verification, it is proved that the proposed method can effectively extract the IF of nonlinear structure and time-varying structure.

키워드

과제정보

Financial support to complete this study is provided in part by the National Natural Science Foundation of China (Grant No. 51979130) and Postdoctoral Research Funding Program of Jiangsu Province (Grant No. 2021K562C). The results and opinions expressed in this paper are those of the authors only and they don't necessarily represent those of the sponsors.

참고문헌

  1. Abratkiewicz, K. (2020), "Double-adaptive chirplet transform for radar signature extraction", IET Radar Sonar Nav., 14(10), 1463-1474. https://doi.org/10.1049/iet-rsn.2020.0084
  2. Angrisani, L. and D'Arco, M. (2002), "A measurement method based on a modified version of the chirplet transform for instantaneous frequency estimation", IEEE T. Instrum. Meas., 51(4), 704-711. https://doi.org/10.1109/TIM.2002.803295
  3. Chen, P., Wang, K., Zuo, M.J. and Wei, D. (2019), "An ameliorated synchroextracting transform based on upgraded local instantaneous frequency approximation", Measurement, 148, 106953. https://doi.org/10.1016/j.measurement.2019.106953
  4. Djurovic, I. and Stankovic, L.J. (2004), "An algorithm for the Wigner distribution based instantaneous frequency estimation in a high noise environment", Signal Process., 84(3), 631-643. https://doi.org/10.1016/j.sigpro.2003.12.006
  5. Guan, Y. and Feng, Z. (2021), "Adaptive linear chirplet transform for analyzing signals with crossing frequency trajectories", IEEE Trans. Ind. Electron., 69(8), 8396-8410. https://doi.org/10.1109/TIE.2021.3097605
  6. Guan, Y., Liang, M. and Necsulescu, D.S. (2018), "Velocity synchronous linear chirplet transform", IEEE Trans. Ind. Electron., 66(8), 6270-6280. https://doi.org/10.1109/TIE.2018.2873520
  7. Hartono, D., Halim, D. and Roberts, G.W. (2019), "Gear fault diagnosis using the general linear chirplet transform with vibration and acoustic measurements", J. Low Freq. Noise V. A., 38(1), 36-52. https://doi.org/10.1177/1461348418811717
  8. Jin, Y., Gao, D. and Ji, H.B. (2017), "Parameter estimation of LFM signals based on synchrosqueezing chirplet transform in complicated noise", J. Electron. Inf. Techn., 39(08), 1906-1912. https://doi.org/10.11999/JEIT161222
  9. Li, Z. and Crocker, M.J. (2006), "A study of joint time-frequency analysis-based modal analysis", IEEE T. Instrum. Meas., 55(6), 2335-2342. https://doi.org/10.1109/TIM.2006.884137
  10. Li, M., Wang, T., Chu, F., Han, Q. and Zhou, M. (2020), "Scaling-basis chirplet transform", IEEE T. Ind. Electron., 68(9), 8777-8788. https://doi.org/10.1109/TIE.2020.3013537
  11. Mann, S. and Haykin, S. (1992), "Adaptive chirplet transform: an adaptive generalization of the wavelet transform", Opt. Eng., 31(6), 1243-1256. https://doi.org/10.1117/12.57676
  12. Mann, S. and Haykin, S. (1995), "The chirplet transform: Physical considerations", IEEE T. Signal Proces., 43(11), 2745-2761. https://doi.org/10.1109/78.482123
  13. Mihovilovic, D. and Bracewell, R.N. (1992), "Whistler analysis in the time-frequency plane using chirplets", J. Geophys. Res.-Space, 97, 17199-17204. https://doi.org/10.1029/92JA01140
  14. Pang, C.S., Liu, L. and Shan, T. (2014), "Time-frequency analysis method based on short time fractional Fourier transform", Acta Electronica Sinica, 42(02), 347-352.
  15. Peng, F., Yu, D. and Luo, J. (2011), "Sparse signal decomposition method based on multi-scale chirplet and its application to gear fault diagnosis", Mech. Syst. Signal Pr., 25(2), 549-557. https://doi.org/10.1016/j.ymssp.2010.06.004
  16. Senthil Pandi, S., Senthilselvi, A., Maragatharajan, M. and Manju, I. (2022), "An optimal self adaptive deep neural network and spine-kernelled chirplet transform for image registration", Concurr. Comp.-Pract. E., 34(27), e7297. https://doi.org/10.1002/cpe.7297
  17. Stockwell, R.G., Mansinha, L. and Lowe, R.P. (1996), "Localization of the complex spectrum: the S transform", IEEE T. Signal Proces., 44(4), 998-1001. https://doi.org/10.1109/78.492555
  18. Yan, H.S., Li, D. and Ding, L.G. (2015), "A new time-frequency analysis approach based on short time Fourier transform", Acta Armamentarii, 36(S2), 258-261.
  19. Yang, Y., Peng, Z.K., Meng, G. and Zhang, W.M. (2011), "Spline-kernelled chirplet transform for the analysis of signals with time-varying frequency and its application", IEEE T. Ind. Electron., 59(3), 1612-1621. https://doi.org/10.1109/TIE.2011.2163376
  20. Yang, Y., Peng, Z.K., Dong, X.J., Zhang, W.M. and Meng, G. (2014), "General parameterized time-frequency transform", IEEE T. Signal Proces., 62(11), 2751-2764. https://doi.org/10.1109/TSP.2014.2314061
  21. Yu, G. and Zhou, Y. (2016), "General linear chirplet transform", Mech. Syst. Signal Pr., 70, 958-973. https://doi.org/10.1016/j.ymssp.2015.09.004
  22. Yuan, P.P., Cheng, X.L., Wang H.H., Zhang, J., Shen, Z.X. and Ren, W.X. (2021), "Structural instantaneous frequency extraction based on improved multi-synchrosqueezing generalized S-transform", Smart Struct. Syst., Int. J., 28(5), 675-687. https://doi.org/10.12989/sss.2021.28.5.675
  23. Yuan, P.P., Zhang, J., Feng, J.Q., Wang, H.H., Ren, W.X. and Wang, C. (2022), "An improved time-frequency analysis method for structural instantaneous frequency identification based on generalized S-transform and synchroextracting transform", Eng. Struct., 252, 113657. https://doi.org/10.1016/j.engstruct.2021.113657
  24. Zhang, L.X., Jia, Y.H., Xu, M.P., Hong, Z., Zhao, X., He, F., Wan, B.K. and Jiao, X.J. (2014), "Chirp stimuli visual evoked potential based brain-computer interface by chirplet transform algorithm", Nanotechnol. Precis. Eng., 12(03), 157-161.
  25. Zhu, X., Zhang, Z., Gao, J. and Li, W. (2019a), "Two robust approaches to multicomponent signal reconstruction from STFT ridges", Mech. Syst. Signal Pr., 115, 720-735. https://doi.org/10.1016/j.ymssp.2018.06.047
  26. Zhu, X., Zhang, Z., Gao, J., Li, B., Li, Z., Huang, X. and Wen, G. (2019b), "Synchroextracting chirplet transform for accurate IF estimate and perfect signal reconstruction", Digit. Signal Process., 93, 172-186. https://doi.org/10.1016/j.dsp.2019.07.015