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The State-of-the-Art on Time-Frequency Signal Processing Techniques for High-Resolution Representation of Nonlinear Systems in Engineering

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Abstract

One of the serious issues of traditional signal processing techniques in analyzing the responses of real-life structures is related to the presentation of fundamental information of nonlinear, non-stationary, and noisy signals with closely-spaced frequencies. To overcome this difficulty, numerous studies have been carried out recently to explore proper time-frequency signal processing techniques to efficiently present high-resolution representations for nonlinear characteristics of analyzed signals. Despite existing extensive reviews on vibration-based signal processing techniques in time and frequency domains for Structural Health Monitoring purposes, there exists no study in categorizing the signal processing techniques based on the feature extraction with time-frequency representations. To fill this gap, this paper presents a comprehensive state-of-the-art review on the applications of time-frequency signal processing techniques for damage detection, localization, and quantification in various structural systems. The progressive trend of time-frequency analysis methods is reviewed by summarizing their advantages and disadvantages, as well as recommendations of combination methods to be utilized for different applications in various complicated structural and mechanical systems.

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References

  1. O’Byrne M, Schoefs F, Ghosh B, Pakrashi V (2013) Texture analysis based damage detection of ageing infrastructural elements. Computer-Aided Civ Infrastruct Eng 28(3):162–177

    Article  Google Scholar 

  2. Amini F, Hazaveh NK, Rad AA (2013) Wavelet PSO-based LQR algorithm for optimal structural control using active tuned mass dampers. Computer‐Aided Civil Infrastructure Eng 28(7):542–557

    Article  Google Scholar 

  3. Katicha SW, Flintsch G, Bryce J, Ferne B (2014) Wavelet denoising of TSD deflection slope measurements for improved pavement structural evaluation. Computer-Aided Civ Infrastruct Eng 29(6):399–415

    Article  Google Scholar 

  4. Amini F, Samani MZ (2014) A wavelet-based adaptive Pole assignment method for structural control. Computer‐Aided Civil Infrastructure Eng 29(6):464–477

    Article  Google Scholar 

  5. Nigro MB, Pakzad SN, Dorvash S (2014) Localized structural damage detection: a change point analysis. Computer-Aided Civ Infrastruct Eng 29(6):416–432

    Article  Google Scholar 

  6. Khalid M, Yusof R, Joshani M, Selamat H, Joshani M (2014) Nonlinear identification of a magneto-rheological damper based on dynamic neural networks. Computer‐Aided Civil Infrastructure Eng 29(3):221–233

    Article  Google Scholar 

  7. Peng ZK, Chu FL (2004) Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech Syst Signal Process 18(2):199–221

    Article  Google Scholar 

  8. Nguyen HN, Kim J, Kim JM (2018) Optimal sub-band analysis based on the envelope power spectrum for effective fault detection in bearing under variable, low speeds. Sensors 18(5):1389

    Article  Google Scholar 

  9. Baek W, Baek S, Kim DY (2018) Characterization of system status signals for multivariate time series discretization based on frequency and amplitude variation. Sensors 18(1):154

    Article  Google Scholar 

  10. Yan R, Gao RX, Chen X (2014) Wavelets for fault diagnosis of rotary machines: a review with applications. Sig Process 96:1–15

    Article  Google Scholar 

  11. Feng Z, Liang M, Chu F (2013) Recent advances in time–frequency analysis methods for machinery fault diagnosis: a review with application examples. Mech Syst Signal Process 38(1):165–205

    Article  Google Scholar 

  12. Lei Y, Lin J, He Z, Zuo MJ (2013) A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech Syst Signal Process 35(1–2):108–126

    Article  Google Scholar 

  13. Zhang C, Mousavi AA, Masri SF, Gholipour G, Yan K, Li X (2022) Vibration feature extraction using signal processing techniques for structural health monitoring: a review. Mech Syst Signal Process 177:109175

    Article  Google Scholar 

  14. Gabor D (1946) Theory of communication. Part 1: the analysis of information. J Institution Electr Engineers-Part III: Radio Communication Eng 93(26):429–441

    Google Scholar 

  15. Cohen L (1995) Time-frequency analysis, vol 778. Prentice hall, New Jersey

    Google Scholar 

  16. Yinfeng D, Yingmin L, Mingkui X, Ming L (2008) Analysis of earthquake ground motions using an improved Hilbert–Huang transform. Soil Dyn Earthq Eng 28(1):7–19

    Article  Google Scholar 

  17. Nagata Y, Iwasaki S, Hariyama T, Fujioka T, Obara T, Wakatake T, Abe M (2009) Binaural localization based on weighted Wiener gain improved by incremental source attenuation. IEEE Trans Audio Speech Lang Process 17(1):52–65

    Article  Google Scholar 

  18. Amezquita-Sanchez JP, Osornio-Rios RA, Romero-Troncoso RJ, Dominguez-Gonzalez A (2012) Hardware-software system for simulating and analyzing earthquakes applied to civil structures. Nat Hazards Earth Syst Sci 12(1):61–73

    Article  Google Scholar 

  19. Yesilyurt I, Gursoy H (2015) Estimation of elastic and modal parameters in composites using vibration analysis. J Vib Control 21(3):509–524

    Article  Google Scholar 

  20. Amezquita-Sanchez JP, Garcia-Perez A, Romero-Troncoso RJ, Osornio-Rios RA, Herrera-Ruiz G (2013) High-resolution spectral-analysis for identifying the natural modes of a truss-type structure by means of vibrations. J Vib Control 19(16):2347–2356

    Article  Google Scholar 

  21. Dolce M, Cardone D (2006) Theoretical and experimental studies for the application of shape memory alloys in civil engineering, 302–311

  22. Cocconcelli M, Zimroz R, Rubini R, Bartelmus W (2012) STFT based approach for ball bearing fault detection in a varying speed motor. Condition monitoring of machinery in non-stationary operations. Springer, Berlin, Heidelberg, pp 41–50

    Chapter  Google Scholar 

  23. Nagarajaiah S, Basu B (2009) Output only modal identification and structural damage detection using time frequency & wavelet techniques. Earthq Eng Eng Vib 8(4):583–605

    Article  Google Scholar 

  24. Nagarajaiah S, Nadathur V, Sahasrabudhe S (1999) Variable Stiffness and Instantaneous Frequency, Proc. Structures Congress, ASCE, New Orleans, 858–861

  25. Nagarajaiah S, Varadarajan N (1997) Semi-active control of smart tuned mass damper using empirical mode decomposition and hilbert transform algorithm. Eng Geol Environ, 205

  26. Nagarajaiah S, Sonmez E (2007) Structures with semiactive variable stiffness single/multiple tuned mass dampers. J Struct Eng 133(1):67–77

    Article  Google Scholar 

  27. Nagarajaiah S (2009) Adaptive passive, semiactive, smart tuned mass dampers: identification and control using empirical mode decomposition, Hilbert transform, and short-term Fourier transform. Struct Control Health Monitoring: Official J Int Association Struct Control Monit Eur Association Control Struct 16(7–8):800–841

    Article  Google Scholar 

  28. Narasimhan S, Nagarajaiah S (2005) A STFT semiactive controller for base isolated buildings with variable stiffness isolation systems. Eng Struct 27(4):514–523

    Article  Google Scholar 

  29. Varadarajan N, Nagarajaiah S (2004) Wind response control of building with variable stiffness tuned mass damper using empirical mode decomposition/Hilbert transform. J Eng Mech 130(4):451–458

    Article  Google Scholar 

  30. Kim BS, Lee SH, Lee MG, Ni J, Song JY, Lee CW (2007) A comparative study on damage detection in speed-up and coast-down process of grinding spindle-typed rotor-bearing system. J Mater Process Technol 187:30–36

    Article  Google Scholar 

  31. Fitzgerald B, Arrigan J, Basu B (2010), July Damage detection in wind turbine blades using time-frequency analysis of vibration signals. In The 2010 International Joint Conference on Neural Networks (IJCNN) (pp. 1–5). IEEE

  32. Mousavi AA, Zhang C, Masri SF, Gholipour G (2020) Structural damage localization and quantification based on a CEEMDAN Hilbert transform neural network approach: a model steel truss bridge case study. Sensors 20(5):1271

    Article  Google Scholar 

  33. Gurley K, Kareem A (1999) Applications of wavelet transforms in earthquake, wind and ocean engineering. Eng Struct 21(2):149–167

    Google Scholar 

  34. Xu YL, Chen B (2008) Integrated vibration control and health monitoring of building structures using semi-active friction dampers: part I—methodology. Eng Struct 30(7):1789–1801

    Article  Google Scholar 

  35. Chen B, Xu YL (2008) Integrated vibration control and health monitoring of building structures using semi-active friction dampers: part II—numerical investigation. Eng Struct 30(3):573–587

    Article  Google Scholar 

  36. Ewins DJ (2009) Modal testing: theory, practice and application. Wiley

  37. Li H, Yi T, Gu M, Huo L (2009) Evaluation of earthquake-induced structural damages by wavelet transform. Prog Nat Sci 19(4):461–470

    Article  Google Scholar 

  38. Yi TH, Li HN, Zhao XY (2012) Noise smoothing for structural vibration test signals using an improved wavelet thresholding technique. Sensors 12(8):11205–11220

    Article  Google Scholar 

  39. Yi TH, Li HN, Gu M (2013) Wavelet based multi-step filtering method for bridge health monitoring using GPS and accelerometer. Smart Struct Syst 11(4):331–348

    Article  Google Scholar 

  40. Huang NE, Long SR, Shen Z (1996) The mechanism for frequency downshift in nonlinear wave evolution. Advances in applied mechanics, vol 32. Elsevier, pp 59–117 C

  41. Huang NE, Shen Z, Long SR (1999) A new view of nonlinear water waves: the Hilbert spectrum. Annu Rev Fluid Mech 31(1):417–457

    Article  MathSciNet  Google Scholar 

  42. Huang NE, Attoh-Okine NO (2005) The Hilbert-Huang transform in engineering. CRC

  43. Vincent HT, Hu SLJ, Hou Z (1999) Damage detection using empirical mode decomposition method and a comparison with wavelet analysis. In Proceedings of the 2nd International Workshop on Structural Health Monitoring (pp. 891–900). Stanford Univeristy, Standford

  44. Yang JN, Lei Y (1999), October Identification of natural frequencies and dam** ratios of linear structures via Hilbert transform and empirical mode decomposition. In Proceedings of the international conference on intelligent systems and control (pp. 310–315). IASTED/Acta Press Anaheim, CA

  45. Yang JN, Lei Y (2001) Damage identification of civil engineering structures using Hilbert-Huang transform. In Proceedings of the 3rd International Workshop on Structural Health Monitoring (pp. 544–553). New York

  46. Chen B, Zhao SL, Li PY (2014) Application of Hilbert-Huang transform in structural health monitoring: a state-of-the-art review. Mathematical Problems in Engineering, 2014

  47. Dong Y, Li Y, Lai M (2010) Structural damage detection using empirical-mode decomposition and vector autoregressive moving average model. Soil Dyn Earthq Eng 30(3):133–145

    Article  Google Scholar 

  48. Cheng-Zhong Q, Xu-Wei L (2012) Damage identification for transmission towers based on HHT. Energy Procedia 17:1390–1394

    Article  Google Scholar 

  49. Rezaei D, Taheri F (2011) Damage identification in beams using empirical mode decomposition. Struct Health Monit 10(3):261–274

    Article  Google Scholar 

  50. Sarmadi H, Entezami A, Daneshvar Khorram M (2020) Energy-based damage localization under ambient vibration and non-stationary signals by ensemble empirical mode decomposition and Mahalanobis-squared distance. J Vib Control 26(11–12):1012–1027

    Article  MathSciNet  Google Scholar 

  51. Entezami A, Shariatmadar H (2019) Structural health monitoring by a new hybrid feature extraction and dynamic time war** methods under ambient vibration and non-stationary signals. Measurement 134:548–568

    Article  Google Scholar 

  52. Fakih MA, Mustapha S, Tarraf J, Ayoub G, Hamade R (2018) Detection and assessment of flaws in friction stir welded joints using ultrasonic guided waves: experimental and finite element analysis. Mech Syst Signal Process 101:516–534

    Article  Google Scholar 

  53. Pines D, Salvino L (2006) Structural health monitoring using empirical mode decomposition and the Hilbert phase. J Sound Vib 294(1–2):97–124

    Article  Google Scholar 

  54. Xu YL, Chen J (2003) Empirical mode decomposition for structural damage detection. In International Conference on Inspection, Appraisal, Repairs and Maintenance of Structures

  55. Bao C, Hao H, Li ZX, Zhu X (2009) Time-varying system identification using a newly improved HHT algorithm. Comput Struct 87(23–24):1611–1623

    Article  Google Scholar 

  56. He XH, Hua XG, Chen ZQ, Huang FL (2011) EMD-based random decrement technique for modal parameter identification of an existing railway bridge. Eng Struct 33(4):1348–1356

    Article  Google Scholar 

  57. Pavlopoulou S, Staszewski WJ, Soutis C (2013) Evaluation of instantaneous characteristics of guided ultrasonic waves for structural quality and health monitoring. Struct Control Health Monit 20(6):937–955

    Article  Google Scholar 

  58. Ghazali MF, Staszewski WJ, Shucksmith JD, Boxall JB, Beck SBM (2011) Instantaneous phase and frequency for the detection of leaks and features in a pipeline system. Struct Health Monit 10(4):351–360

    Article  Google Scholar 

  59. Esmaeel RA, Taheri F (2012) Delamination detection in laminated composite beams using the empirical mode decomposition energy damage index. Compos Struct 94(5):1515–1523

    Article  Google Scholar 

  60. Alvanitopoulos PF, Andreadis I, Elenas A (2009) Interdependence between damage indices and ground motion parameters based on Hilbert–Huang transform. Meas Sci Technol 21(2):025101

    Article  Google Scholar 

  61. Wei YC, Lee CJ, Hung WY, Chen HT (2010) Application of Hilbert-Huang transform to characterize soil liquefaction and quay wall seismic responses modeled in centrifuge shaking-table tests. Soil Dyn Earthq Eng 30(7):614–629

    Article  Google Scholar 

  62. Shi W, Shan J, Lu X (2012) Modal identification of Shanghai World Financial Center both from free and ambient vibration response. Eng Struct 36:14–26

    Article  Google Scholar 

  63. Garcia-Perez A, Amezquita-Sanchez JP, Dominguez-Gonzalez A, Sedaghati R, Osornio-Rios R, Romero-Troncoso RJ (2013) Fused empirical mode decomposition and wavelets for locating combined damage in a truss-type structure through vibration analysis. J Zhejiang Univ Sci A 14(9):615–630

    Article  Google Scholar 

  64. Chiou DJ, Hsu WK, Chen CW, Hsieh CM, Tang JP, Chiang WL (2011) Applications of Hilbert-Huang transform to structural damage detection. Struct Eng Mechanics: Int J 39(1):1–20

    Article  Google Scholar 

  65. Lin L, Chu F (2011) Feature extraction of AE characteristics in offshore structure model using Hilbert–Huang transform. Measurement 44(1):46–54

    Article  MathSciNet  Google Scholar 

  66. Hamdi SE, Le Duff A, Simon L, Plantier G, Sourice A, Feuilloy M (2013) Acoustic emission pattern recognition approach based on Hilbert–Huang transform for structural health monitoring in polymer-composite materials. Appl Acoust 74(5):746–757

    Article  Google Scholar 

  67. Lin CC, Liu PL, Yeh PL (2009) Application of empirical mode decomposition in the impact-echo test. Ndt E Int 42(7):589–598

    Article  Google Scholar 

  68. Yadav SK, Banerjee S, Kundu T (2011) Effective damage sensitive feature extraction methods for crack detection using flaw scattered ultrasonic wave field signal. In 8th International Workshop on Structural Health Monitoring 2011: Condition-Based Maintenance and Intelligent Structures, 167–174

  69. Meredith J, González A, Hester D (2012) Empirical mode decomposition of the acceleration response of a prismatic beam subject to a moving load to identify multiple damage locations. Shock Vib 19(5):845–856

    Article  Google Scholar 

  70. Roveri N, Carcaterra A (2012) Damage detection in structures under traveling loads by Hilbert–Huang transform. Mech Syst Signal Process 28:128–144

    Article  Google Scholar 

  71. Yu DJ, Ren WX (2005) EMD-based stochastic subspace identification of structures from operational vibration measurements. Eng Struct 27(12):1741–1751

    Article  Google Scholar 

  72. Ma L, Liu JX, Han WS, Ji BH (2010) Time-frequency analysis for nonlinear buffeting response of a long-span bridge based on HHT. Journal of Vibration and Shock

  73. Chen B, Chen ZW, Sun YZ, Zhao SL (2013) Condition assessment on thermal effects of a suspension bridge based on SHM oriented model and data. Mathematical Problems in Engineering, 2013

  74. Zhang X, Du X, Brownjohn J (2012) Frequency modulated empirical mode decomposition method for the identification of instantaneous modal parameters of aeroelastic systems. J Wind Eng Ind Aerodyn 101:43–52

    Article  Google Scholar 

  75. Yi J, Zhang JW, Li QS (2013) Dynamic characteristics and wind-induced responses of a super-tall building during typhoons. J Wind Eng Ind Aerodyn 121:116–130

    Article  Google Scholar 

  76. Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt data Anal 1(01):1–41

    Article  Google Scholar 

  77. Liu TY, Chiang WL, Chen CW, Hsu WK, Lu LC, Chu TJ (2014) Identification and monitoring of bridge health from ambient vibration data (retraction of 17, pg 589, 2011). J Vib Control 20(10):1604–1604

    Google Scholar 

  78. Torres ME, Colominas MA, Schlotthauer G, Flandrin P (2011), May A complete ensemble empirical mode decomposition with adaptive noise. In 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 4144–4147). IEEE

  79. Wang T, Zhang M, Yu Q, Zhang H (2012) Comparing the applications of EMD and EEMD on time–frequency analysis of seismic signal. J Appl Geophys 83:29–34

    Article  Google Scholar 

  80. Lin JW (2011) A hybrid algorithm based on EEMD and EMD for multi-mode signal processing. Struct Eng Mech 39(6):813–831

    Article  Google Scholar 

  81. Camarena-Martinez D, Amezquita-Sanchez JP, Valtierra-Rodriguez M, Romero-Troncoso RJ, Osornio-Rios RA, Garcia-Perez A (2014) EEMD-MUSIC-based analysis for natural frequencies identification of structures using artificial and natural excitations. The Scientific World Journal, 2014

  82. Amiri GG, Darvishan E (2015) Damage detection of moment frames using ensemble empirical mode decomposition and clustering techniques. KSCE J Civ Eng 19(5):1302–1311

    Article  Google Scholar 

  83. Lei Y, Liu Z, Ouazri J, Lin J (2017) A fault diagnosis method of rolling element bearings based on CEEMDAN. Proc Institution Mech Eng Part C: J Mech Eng Sci 231(10):1804–1815

    Article  Google Scholar 

  84. Liu B, Riemenschneider S, Xu Y (2006) Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum. Mech Syst Signal Process 20(3):718–734

    Article  Google Scholar 

  85. Mohanty S, Gupta KK, Raju KS (2016) Vibro-acoustic fault analysis of bearing using FFT, EMD, EEMD and CEEMDAN and their implications. Advances in machine learning and signal processing. Springer, Cham, pp 281–292

    Chapter  Google Scholar 

  86. Lei Y, He Z, Zi Y (2009) Application of the EEMD method to rotor fault diagnosis of rotating machinery. Mech Syst Signal Process 23(4):1327–1338

    Article  Google Scholar 

  87. Georgoulas G, Loutas T, Stylios CD, Kostopoulos V (2013) Bearing fault detection based on hybrid ensemble detector and empirical mode decomposition. Mech Syst Signal Process 41(1–2):510–525

    Article  Google Scholar 

  88. Han J, Van der Baan M (2013) Empirical mode decomposition for seismic time-frequency analysis. Geophysics 78(2):O9–O19

    Article  Google Scholar 

  89. Mousavi AA, Zhang C, Masri SF, Gholipour G (2021) Structural damage detection method based on the complete ensemble empirical mode decomposition with adaptive noise: a model steel truss bridge case study. Struct Health Monit, 14759217211013535

  90. Mousavi AA, Zhang C, Masri SF, Gholipour G (2021) Damage detection and characterization of a scaled model steel truss bridge using combined complete ensemble empirical mode decomposition with adaptive noise and multiple signal classification approach. Struct Health Monit, 14759217211045901

  91. **ao F, Chen GS, Zatar W, Hulsey JL (2021) Signature extraction from the dynamic responses of a bridge subjected to a moving vehicle using complete ensemble empirical mode decomposition. J Low Freq Noise Vib Act Control 40(1):278–294

    Article  Google Scholar 

  92. Li Y, Chen X, Yu J (2019) A hybrid energy feature extraction approach for ship-radiated noise based on CEEMDAN combined with energy difference and energy entropy. Processes 7(2):69

    Article  Google Scholar 

  93. Lv Y, Yuan R, Wang T, Li H, Song G (2018) Health degradation monitoring and early fault diagnosis of a rolling bearing based on CEEMDAN and improved MMSE. Materials 11(6):1009

    Article  Google Scholar 

  94. Kuai M, Cheng G, Pang Y, Li Y (2018) Research of planetary gear fault diagnosis based on permutation entropy of CEEMDAN and ANFIS. Sensors 18(3):782

    Article  Google Scholar 

  95. Sifuzzaman M, Islam MR, Ali MZ (2009) Application of Wavelet transform and its advantages compared to Fourier Transform. J Phys Sci 13:121–134

    Google Scholar 

  96. Sifuzzaman M, Islam MR, Ali MZ (2009) Application of wavelet transform and its advantages compared to Fourier transform

  97. Okafor AC, Dutta A (2000) Structural damage detection in beams by wavelet transforms. Smart Mater Struct 9(6):906

    Article  Google Scholar 

  98. Yoon DJ, Weiss WJ, Shah SP (2000) Assessing damage in corroded reinforced concrete using acoustic emission. J Eng Mech 126(3):273–283

    Article  Google Scholar 

  99. Melhem H, Kim H (2003) Damage detection in concrete by Fourier and wavelet analyses. J Eng Mech 129(5):571–577

    Article  Google Scholar 

  100. Park S, Inman DJ, Lee JJ, Yun CB (2008) Piezoelectric sensor-based health monitoring of railroad tracks using a two-step support vector machine classifier. J Infrastruct Syst 14(1):80–88

    Article  Google Scholar 

  101. Young Noh H, Nair K, Lignos K, D. G., Kiremidjian AS (2011) Use of wavelet-based damage-sensitive features for structural damage diagnosis using strong motion data. J Struct Eng 137(10):1215–1228

    Article  Google Scholar 

  102. Su WC, Liu CY, Huang CS (2014) Identification of instantaneous modal parameter of time-varying systems via a wavelet‐based approach and its application. Computer‐Aided Civil Infrastructure Eng 29(4):279–298

    Article  Google Scholar 

  103. Li S, Li H, Liu Y, Lan C, Zhou W, Ou J (2014) SMC structural health monitoring benchmark problem using monitored data from an actual cable-stayed bridge. Struct Control Health Monit 21(2):156–172

    Article  Google Scholar 

  104. Gaviria CA, Montejo LA (2016) Output-only identification of the modal and physical properties of structures using free vibration response. Earthq Eng Eng Vib 15(3):575–589

    Article  Google Scholar 

  105. Gholizad A, Safari H (2016) Two-dimensional continuous wavelet transform method for multidamage detection of space structures. J Perform Constr Facil 30(6):04016064

    Article  Google Scholar 

  106. Shahsavari V, Chouinard L, Bastien J (2017) Wavelet-based analysis of mode shapes for statistical detection and localization of damage in beams using likelihood ratio test. Eng Struct 132:494–507

    Article  Google Scholar 

  107. Abdulkareem M, Bakhary N, Vafaei M, Noor NM, Padil KH (2018) Non-probabilistic wavelet method to consider uncertainties in structural damage detection. J Sound Vib 433:77–98

    Article  Google Scholar 

  108. Wang S, Li J, Luo H, Zhu H (2019) Damage identification in underground tunnel structures with wavelet based residual force vector. Eng Struct 178:506–520

    Article  Google Scholar 

  109. Pan H, Azimi M, Yan F, Lin Z (2018) Time-frequency-based data-driven structural diagnosis and damage detection for cable-stayed bridges. J Bridge Engineering 23(6):04018033

    Article  Google Scholar 

  110. Karami-Mohammadi R, Mirtaheri M, Salkhordeh M, Hariri-Ardebili MA (2020) Vibration anatomy and damage detection in power transmission towers with limited sensors. Sensors 20(6):1731

    Article  Google Scholar 

  111. Hou Z, Noori M, Amand RS (2000) Wavelet-based approach for structural damage detection. J Eng Mech 126(7):677–683

    Article  Google Scholar 

  112. Hera A, Hou Z (2004) Application of wavelet approach for ASCE structural health monitoring benchmark studies. J Eng Mech 130(1):96–104

    Article  Google Scholar 

  113. Ovanesova AV, Suarez LE (2004) Applications of wavelet transforms to damage detection in frame structures. Eng Struct 26(1):39–49

    Article  Google Scholar 

  114. Hoseini Vaez SR, Tabaei Aghdaei SS (2019) Effect of the frequency content of earthquake excitation on damage detection in steel frames. J Rehabilitation Civil Eng 7(1):124–140

    Google Scholar 

  115. Feng X, Zhang X, Sun C, Motamedi M, Ansari F (2014) Stationary wavelet transform method for distributed detection of damage by fiber-optic sensors. J Eng Mech 140(4):04013004

    Article  Google Scholar 

  116. Lucero J, Taha MR (2005) A wavelet-aided fuzzy damage detection algorithm for structural health monitoring. In Proceedings of the 23rd international. modal analysis conference. (IMAX XXIII), Paper (No. 78)

  117. Djebala A, Ouelaa N, Benchaabane C, Laefer DF (2012) Application of the wavelet multi-resolution analysis and Hilbert transform for the prediction of gear tooth defects. Meccanica 47(7):1601–1612

    Article  Google Scholar 

  118. Datta A, Mavroidis C, Krishnasamy J, Hosek M (2007), July Neural netowrk based fault diagnostics of industrial robots using wavelt multi-resolution analysis. In 2007 American control conference (pp. 1858–1863). IEEE

  119. Zhang W, Sun L, Zhang L (2018) Local damage identification method using finite element model updating based on a new wavelet damage function. Adv Struct Eng 21(10):1482–1494

    Article  Google Scholar 

  120. Saltari F, Dessi D, Mastroddi F (2021) Mechanical systems virtual sensing by proportional observer and multi-resolution analysis. Mech Syst Signal Process 146:107003

    Article  Google Scholar 

  121. Sun Z, Chang CC (2002) Structural damage assessment based on wavelet packet transform. J Struct Eng 128(10):1354–1361

    Article  Google Scholar 

  122. Yam LH, Yan YJ, Jiang JS (2003) Vibration-based damage detection for composite structures using wavelet transform and neural network identification. Compos Struct 60(4):403–412

    Article  Google Scholar 

  123. Han JG, Ren WX, Sun ZS (2005) Wavelet packet based damage identification of beam structures. Int J Solids Struct 42(26):6610–6627

    Article  Google Scholar 

  124. Ren WX, Sun ZS, **a Y, Hao H, Deeks AJ (2008) Damage identification of shear connectors with wavelet packet energy: laboratory test study. J Struct Eng 134(5):832–841

    Article  Google Scholar 

  125. Asgarian B, Aghaeidoost V, Shokrgozar HR (2016) Damage detection of jacket type offshore platforms using rate of signal energy using wavelet packet transform. Mar Struct 45:1–21

    Article  Google Scholar 

  126. Chan CK, Loh CH, Wu TH (2015), April Damage detection and quantification in a structural model under seismic excitation using time-frequency analysis. In Structural Health Monitoring and Inspection of Advanced Materials, Aerospace, and Civil Infrastructure 2015 (Vol. 9437, p. 94372L). International Society for Optics and Photonics

  127. Nie Z, Guo E, Ma H (2019) Structural damage detection using wavelet packet transform combining with principal component analysis. Int J Lifecycle Perform Eng 3(2):149–170

    Article  Google Scholar 

  128. Daubechies I, Lu J, Wu HT (2011) Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl Comput Harmon Anal 30(2):243–261

    Article  MathSciNet  Google Scholar 

  129. Wen J, Gao H, Li S, Zhang L, He X, Liu W (2015), October Fault diagnosis of ball bearings using synchrosqueezed wavelet transforms and SVM. In 2015 Prognostics and System Health Management Conference (PHM) (pp. 1–6). IEEE

  130. Perez-Ramirez CA, Amezquita-Sanchez JP, Adeli H, Valtierra-Rodriguez M, Camarena-Martinez D, Romero-Troncoso RJ (2016) New methodology for modal parameters identification of smart civil structures using ambient vibrations and synchrosqueezed wavelet transform. Eng Appl Artif Intell 48:1–12

    Article  Google Scholar 

  131. Amezquita-Sanchez JP, Adeli H (2015) Synchrosqueezed wavelet transform-fractality model for locating, detecting, and quantifying damage in smart highrise building structures. Smart Mater Struct 24(6):065034

    Article  Google Scholar 

  132. Wang J, Huo L, Liu C, Song G (2021) A new acoustic emission damage localization method using synchrosqueezed wavelet transforms picker and time-order method. Struct Health Monit 20(6):2917–2935

    Article  Google Scholar 

  133. Li D, Wang Y, Yan WJ, Ren WX (2021) Acoustic emission wave classification for rail crack monitoring based on synchrosqueezed wavelet transform and multi-branch convolutional neural network. Struct Health Monit 20(4):1563–1582

    Article  Google Scholar 

  134. Su C, Jiang M, Liang J, Tian A, Sun L, Zhang L, Sui Q (2020) Damage assessments of composite under the environment with strong noise based on synchrosqueezing wavelet transform and stack autoencoder algorithm. Measurement 156:107587

    Article  Google Scholar 

  135. Gilles J (2013) Empirical wavelet transform. IEEE Trans Signal Process 61:3999–4010

    Article  MathSciNet  Google Scholar 

  136. Gilles J, Heal K (2014) A parameterless scale-space approach to find meaningful modes in histograms—application to image and spectrum segmentation. Int J Wavelets Multiresolut Inf Process 12(06):1450044

    Article  MathSciNet  Google Scholar 

  137. Zheng J, Pan H, Yang S, Cheng J (2017) Adaptive parameterless empirical wavelet transform based time-frequency analysis method and its application to rotor rubbing fault diagnosis. Sig Process 130:305–314

    Article  Google Scholar 

  138. Liu T, Luo Z, Huang J, Yan S (2018) A comparative study of four kinds of adaptive decomposition algorithms and their applications. Sensors 18(7):2120

    Article  Google Scholar 

  139. Kedadouche M, Thomas M, Tahan AJMS (2016) A comparative study between empirical Wavelet transforms and empirical Mode decomposition methods: application to bearing defect diagnosis. Mech Syst Signal Process 81:88–107

    Article  Google Scholar 

  140. Amezquita-Sanchez JP, Adeli H (2015) A new music-empirical wavelet transform methodology for time–frequency analysis of noisy nonlinear and non-stationary signals. Digit Signal Proc 45:55–68

    Article  Google Scholar 

  141. Reddy GRS, Rao R (2016) Empirical Wavelet Transform Based Approach for extraction of fundamental component and estimation of time-varying Power Quality indices in Power Quality disturbances. Int J Signal Process Image Process Pattern Recognit 9:161–180

    Google Scholar 

  142. Thirumala K, Jain T, Umarikar AC (2017) Visualizing time-varying power quality indices using generalized empirical wavelet transform. Electr Power Syst Res 143:99–109

    Article  Google Scholar 

  143. Liu T, Li J, Cai X, Yan S (2018) A time-frequency analysis algorithm for ultrasonic waves generating from a debonding defect by using empirical wavelet transform. Appl Acoust 131:16–27

    Article  Google Scholar 

  144. Yuan M, Sadhu A, Liu K (2018) Condition assessment of structure with tuned mass damper using empirical wavelet transform. J Vib Control 24(20):4850–4867

    Article  Google Scholar 

  145. **n Y, Hao H, Li J (2019) Operational modal identification of structures based on improved empirical wavelet transform. Struct Control Health Monit, 26(3), e2323

  146. Amezquita-Sanchez JP, Park HS, Adeli H (2017) A novel methodology for modal parameters identification of large smart structures using MUSIC, empirical wavelet transform, and Hilbert transform. Eng Struct 147:148–159

    Article  Google Scholar 

  147. Yadav SK, Banerjee S, Kundu T (2013) On sequencing the feature extraction techniques for online damage characterization. J Intell Mater Syst Struct 24(4):473–483

    Article  Google Scholar 

  148. Amjad U, Yadav SK, Kundu T (2015) Detection and quantification of diameter reduction due to corrosion in reinforcing steel bars. Struct Health Monit 14(5):532–543

    Article  Google Scholar 

  149. Pakrashi V, Ghosh B (2009) Application of S transform in structural health monitoring. In 7th International Symposium on Nondestructive Testing in Civil Engineering (NDTCE). Nantes, France 30 Jun-3 Jul 2009

  150. Ditommaso R, Mucciarelli M, Parolai S, Picozzi M (2012) Monitoring the structural dynamic response of a masonry tower: comparing classical and time-frequency analyses. Bull Earthq Eng 10(4):1221–1235

    Article  Google Scholar 

  151. Amini Tehrani H, Bakhshi A, Akhavat M (2017) An effective approach to structural damage localization in flexural members based on generalized S-transform. Scientia Iranica 26(6):3125–3139

    Google Scholar 

  152. Liu N, ** factor of reinforced concrete framed structures using the short time impulse response function (STIRF). Eng Struct 82:104–112

    Article  Google Scholar 

  153. Brown RA, Frayne R (2008), August A fast discrete S-transform for biomedical signal processing. In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 2586–2589). IEEE

  154. Ghahremani B, Bitaraf M, Ghorbani-Tanha AK, Fallahi R (2021, February) Structural damage identification based on fast S-transform and convolutional neural networks. Structures, vol 29. Elsevier, pp 1199–1209

  155. Cohen L (1966) Generalized phase-space distribution functions. J Math Phys 7(5):781–786

    Article  MathSciNet  Google Scholar 

  156. Staszewski WJ, Worden K, Tomlinson GR (1997) Time–frequency analysis in gearbox fault detection using the Wigner–Ville distribution and pattern recognition. Mech Syst Signal Process 11(5):673–692

    Article  Google Scholar 

  157. Claasen TACM, Mecklenbräuker W (1980) Time-frequency signal analysis. Philips J Res 35(6):372–389

    MathSciNet  Google Scholar 

  158. Baydar N, Ball A (2001) A comparative study of acoustic and vibration signals in detection of gear failures using Wigner–Ville distribution. Mech Syst Signal Process 15(6):1091–1107

    Article  Google Scholar 

  159. Zou J, Chen J (2004) A comparative study on time–frequency feature of cracked rotor by Wigner–Ville distribution and wavelet transform. J Sound Vib 276(1–2):1–11

    Article  Google Scholar 

  160. Gillich GR, Praisach ZI (2014) Modal identification and damage detection in beam-like structures using the power spectrum and time–frequency analysis. Sig Process 96:29–44

    Article  Google Scholar 

  161. Dai D, He Q (2014) Structure damage localization with ultrasonic guided waves based on a time–frequency method. Sig Process 96:21–28

    Article  Google Scholar 

  162. Katunin A (2020) Damage identification and quantification in beams using Wigner-Ville distribution. Sensors 20(22):6638

    Article  Google Scholar 

  163. Qatu KM, Abdelgawad A, Yelamarthi K (2016), March Structure damage localization using a reliable wave damage detection technique. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (pp. 1959–1962). IEEE

  164. Yang JN, Lei Y (2000) System Identification of Linear Structures Using Hilbert Transform and Empirical Mode Decomposition, # 256. In Proceedings of IMAC-XVIII: A Conference on Structural Dynamics (Vol. 4062, p. 213)

  165. Wu J, Ma Z, Zhang Y (2017) A time-frequency research for ultrasonic guided wave generated from the debonding based on a novel time-frequency analysis technique. Shock and vibration, 2017

  166. Bao C, Hao H, Li ZX (2013) Multi-stage identification scheme for detecting damage in structures under ambient excitations. Smart Mater Struct 22(4):045006

    Article  Google Scholar 

  167. Mallat S (1999) A Wavelet Tour of Signal Processing, 2nd edition, London, UK: Academic Press

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Acknowledgements

This research is financially supported by the National Natural Science Foundation of China (Grant No. 52361135807), the Department of Science and Technology of Shandong Province (Grant No. 2021CXGC011204). and the Liaoning provincial key laboratory of Safety and Protection for Infrastructure Engineering.

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Zhang, C., Mousavi, A.A., Masri, S.F. et al. The State-of-the-Art on Time-Frequency Signal Processing Techniques for High-Resolution Representation of Nonlinear Systems in Engineering. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10153-z

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