Structural Monitoring and Maintenance

  • 제8권4호
  • /
  • Pages.379-402
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  • 2021
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  • 2288-6605(pISSN)
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  • 2288-6613(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Structural damage detection in presence of temperature variability using 2D CNN integrated with EMD

  • Sharma, Smriti (i4S Laboratory, Indian Institute of Technology Mandi) ;
  • Sen, Subhamoy (i4S Laboratory, Indian Institute of Technology Mandi)
  • 투고 : 2021.07.22
  • 심사 : 2021.12.17
  • 발행 : 2021.12.25
⟨ 이전 논문 다음 논문 ⟩

초록

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.

키워드

과제정보

This study was funded by Aeronautics Research & Development Board (DRDO), New Delhi, India through grant file no. ARDB/01/1051907/M/I.

참고문헌

  1. Abdel-Hamid, O., Mohamed, A.R., Jiang, H. and Penn, G. (2012), "Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition", Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, March, pp. 4277-4280. https://doi.org/10.1109/ICASSP.2012.6288864
  2. Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M. and Inman, D.J. (2017), "Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks", J. Sound Vib., 388, 154-170. https://doi.org/10.1016/j.jsv.2016.10.043
  3. Abdeljaber, O., Avci, O., Kiranyaz, M.S., Boashash, B., Sodano, H. and Inman, D.J. (2018), "1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data", Neurocomputing, 275, 1308-1317. https://doi.org/10.1016/j.neucom.2017.09.069
  4. Alampalli, S. (2000), "Effects of testing, analysis, damage, and environment on modal parameters", Mech. Syst. Signal Process., 14(1), 63-74. https://doi.org/10.1006/mssp.1999.1271
  5. Avci, O., Abdeljaber, O., Kiranyaz, S. and Inman, D. (2017), "Structural damage detection in real time: Implementation of 1D convolutional neural networks for SHM applications", Proceedings of the Society for Experimental Mechanics Series, Volume 7, pp. 49-54. https://doi.org/10.1007/978-3-319-54109-9_6
  6. Cha, Y.J., Choi, W. and Buyukozturk, O. (2017), "Deep learning-based crack damage detection using convolutional neural networks", Comput.-Aided Civil Infrastr. Eng., 32(5), 361-378. https://doi.org/10.1111/mice.12263
  7. Charleston-Villalobos, S., Gonzalez-Camarena, R., Chi-Lem, G. and Aljama-Corrales, T. (2007), "Crackle sounds analysis by empirical mode decomposition: Nonlinear and nonstationary signal analysis for distinction of crackles in lung sounds", IEEE Engineering in Medicine and Biology Magazine: The Quarterly Magazine of the Engineering in Medicine & Biology Society, 26(1), pp. 40-47. https://doi.org/10.1109/MEMB.2007.289120
  8. Cornwell, P., Farrar, C.R., Doebling, S.W. and Sohn, H. (1999), "Environmental variability of modal properties", Experim. Techniq., 23(6), 45-48. https://doi.org/10.1111/j.1747-1567.1999.tb01320.x
  9. LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W. and Jackel, L. (1989a), "Handwritten digit recognition with a back-propagation network", Adv. Neural Inform. Process. Syst., 2.
  10. Le Cun, Y., Jackel, L.D., Boser, B., Denker, J.S., Graf, H.P., Guyon, I., Henderson, D., Howard, R.E. and Hubbard, W. (1989b), "Handwritten digit recognition: Applications of neural network chips and automatic learning", IEEE Commun. Magazine, 27(11), 41-46. https://doi.org/10.1109/35.41400
  11. Datig, M. and Schlurmann, T. (2004), "Performance and limitations of the Hilbert-Huang transformation (HHT) with an application to irregular water waves", Ocean Eng., 31(14-15), 1783-1834. https://doi.org/10.1016/j.oceaneng.2004.03.007
  12. De Oliveira, M.A., Araujo, N.V., Da Silva, R.N., Da Silva, T.I. and Epaarachchi, J. (2018), "Use of Savitzky-Golay filter for performances improvement of SHM systems based on neural networks and distributed PZT sensors", Sensors, 18(1), 152. https://doi.org/10.3390/s18010152
  13. dos Santos, F.L.M., Peeters, B., Lau, J., Desmet, W. and Goes, L.C.S. (2015), "The use of strain gauges in vibration-based damage detection The use of strain gauges in vibration-based damage detection", J. Physics: Conference Series, 628(1), 012119. https://doi.org/10.1088/1742-6596/628/1/012119
  14. Duque, A.B., Santos, L.L.J., Macedo, D. and Zanchettin, C. (2019), "Squeezed very deep convolutional neural networks for text classification", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 11727, pp. 193-207. https://doi.org/10.1007/978-3-030-30487-4_16
  15. Flandrin, P., Rilling, G. and Goncalves, P. (2004), "Empirical mode decomposition as a filter bank", IEEE Signal Process. Lett., 11(2), 112-114. https://doi.org/10.1109/LSP.2003.821662
  16. Gavin, H. (2012), "Geometric stiffness effects in 2D and 3D frames", Department of Civil and Environment Engineering, Edmund T. Pratt School of Engineering, Duke University, pp. 1-14.
  17. Glisic, B., Chen, J. and Hubbell, D. (2011), "Streicker Bridge: a comparison between Bragg-grating longgauge strain and temperature sensors and Brillouin scattering-based distributed strain and temperature sensors", In: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011, Volume 7981, p. 79812C. https://doi.org/10.1117/12.881818
  18. Gulgec, N.S., Takac, M. and Pakzad, S.N. (2019), "Convolutional neural network approach for robust structural damage detection and localization", J. Comput. Civil Eng., 33(3), p. 04019005. https://doi.org/10.1061/(asce)cp.1943-5487.0000820
  19. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C. and Liu, H.H. (1998), "The empirical mode decomposition and the Hubert spectrum for nonlinear and non-stationary time series analysis", Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454(1971), pp. 903-995. https://doi.org/10.1098/rspa.1998.0193
  20. Ince, T., Kiranyaz, S., Eren, L., Askar, M. and Gabbouj, M. (2016), "Real-time motor fault detection by 1-D convolutional neural networks", IEEE Transact. Indust. Electron., 63(11), 7067-7075. https://doi.org/10.1109/TIE.2016.2582729
  21. Jin, C., Jang, S., Sun, X., Li, J. and Christenson, R. (2016), "Damage detection of a highway bridge under severe temperature changes using extended Kalman filter trained neural network", J. Civil Struct. Health Monitor., 6(3), 545-560. https://doi.org/10.1007/s13349-016-0173-8
  22. Khoury, G.A., Grainger, B.N. and Sullivan, J.E. (1985), "Transient thermal strain of concrete: literature review, conditions within specimen and behaviour of individual constituents", Magaz. Concrete Res., 37(132), pp. 131-144. https://doi.org/10.1680/macr.1985.37.132.131
  23. Kiranyaz, S., Ince, T. and Gabbouj, M. (2016), "Real-time patient-specific ECG classification by 1-D convolutional neural networks", IEEE Transact. Biomed. Eng., 63(3), 664-675. https://doi.org/10.1109/TBME.2015.2468589
  24. Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012), "Imagenet classification with deep convolutional neural networks", Adv. Neural Inform. Process. Syst., 25, 1097-1105.
  25. Kromanis, R., Kripakaran, P. and Harvey, B. (2016), "Long-term structural health monitoring of the Cleddau bridge: Evaluation of quasi-static temperature effects on bearing movements", Struct. Infrastr. Eng., 12(10), 1342-1355. https://doi.org/10.1080/15732479.2015.1117113
  26. Kullaa, J. (2009), "Eliminating environmental or operational influences in structural health monitoring using the missing data analysis", J. Intell. Mater. Syst. Struct., 20(11), 1381-1390. https://doi.org/10.1177/1045389X08096050
  27. Le Cun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W. and Jackel, L. (1989), "Handwritten digit recognition with a back-propagation network", Adv. Neural Inform. Process. Syst., 2.
  28. Li, H.L., Deng, X. and Dai, H. (2007), "Structural damage detection using the combination method of EMD and wavelet analysis", Mech. Syst. Signal Process., 21(1), 298-306. https://doi.org/10.1016/j.ymssp.2006.05.001
  29. Rabbath, C.A. and Corriveau, D. (2019), "A comparison of piecewise cubic Hermite interpolating polynomials, cubic splines and piecewise linear functions for the approximation of projectile aerodynamics", Defence Technology, 15(5), 741-757. https://doi.org/10.1016/j.dt.2019.07.016
  30. Reddy, J.N. and Chin, C.D. (1998), "Thermomechanical analysis of functionally graded cylinders and plates", J. Thermal Stresses, 21(6), 593-626. https://doi.org/10.1080/01495739808956165
  31. Rezaei, D. and Taheri, F. (2011), Structural Health Monitoring, John Wiley & Sons. https://doi.org/10.1177/1475921710373298
  32. Sharma, S. and Sen, S. (2020), "One-dimensional convolutional neural network-based damage detection in structural joints", J. Civil Struct. Health Monitor., 10(5), 1057-1072. https://doi.org/10.1007/s13349-020-00434-z
  33. Sharma, S. and Sen, S. (2021a), "Bridge Damage Detection in Presence of Varying Temperature Using Two-Step Neural Network Approach", J. Bridge Eng., 26(6), 04021027. https://doi.org/10.1061/(asce)be.1943-5592.0001708
  34. Sharma, S. and Sen, S. (2021b), "Damage Detection in Presence of Varying Temperature Using Mode Shape and a Two-Step Neural Network", In: Recent Advances in Computational Mechanics and Simulations, pp. 285-299. https://doi.org/10.1007/978-981-15-8138-0_23
  35. Sharma, R., Vignolo, L., Schlotthauer, G., Colominas, M.A., Rufiner, H.L. and Prasanna, S.R.M. (2017), "Empirical mode decomposition for adaptive AM-FM analysis of speech: A review", Speech Communication, 88, 39-64. https://doi.org/10.1016/j.specom.2016.12.004
  36. Shulin, L., Haifeng, Z., Hui, W. and Rui, M. (2007), "Application of improved EMD algorithm for the fault diagnosis of reciprocating pump valves with spring failure", In: 2007 9th International Symposium on Signal Processing and Its Applications, pp. 1-4. https://doi.org/10.1109/ISSPA.2007.4555473
  37. Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W., Nadler, B.R. and Czarnecki, J.J. (2003), "A review of structural health monitoring literature: 1996-2001", Los Alamos National Laboratory, USA, 1.
  38. Srinivasan, R., Rengaswamy, R. and Miller, R. (2007), "A modified empirical mode decomposition (EMD) process for oscillation characterization in control loops", Control Eng. Practice, 15(9), 1135-1148. https://doi.org/10.1016/j.conengprac.2007.01.014
  39. Sun, L., Shang, Z., Xia, Y., Bhowmick, S. and Nagarajaiah, S. (2020), "Review of bridge structural health monitoring aided by big data and artificial intelligence: From condition assessment to damage detection", J. Struct. Eng., 146(5), 04020073. https://doi.org/10.1061/(asce)st.1943-541x.0002535
  40. Weinstein, J.C., Sanayei, M. and Brenner, B.R. (2018), "Bridge damage identification using artificial neural networks", J. Bridge Eng., 23(11), 04018084. https://doi.org/10.1061/(asce)be.1943-5592.0001302
  41. Xia, Q., Cheng, Y., Zhang, J. and Zhu, F. (2017), "In-service condition assessment of a long-span suspension bridge using temperature-induced strain data", J. Bridge Eng., 22(3), 04016124. https://doi.org/10.1061/(asce)be.1943-5592.0001003
  42. Xu, Y.L. and Chen, J. (2004), "Structural damage detection using empirical mode decomposition: experimental investigation", J. Eng. Mech., 130(11), 1279-1288. https://doi.org/10.1061/(asce)0733-9399(2004)130:11(1279)
  43. Xun, J. and Yan, S. (2008), "A revised Hilbert-Huang transformation based on the neural networks and its application in vibration signal analysis of a deployable structure", Mech. Syst. Signal Process., 22(7), 1705-1723. https://doi.org/10.1016/j.ymssp.2008.02.008
  44. Yan, A.M., Kerschen, G., De Boe, P. and Golinval, J.C. (2005), "Structural damage diagnosis under varying environmental conditions - Part I: A linear analysis", Mech. Syst. Signal Process., 19(4), 847-864. https://doi.org/10.1016/j.ymssp.2004.12.002
  45. Yang, Y.B. and Chang, K.C. (2009), "Extraction of bridge frequencies from the dynamic response of a passing vehicle enhanced by the EMD technique", J. Sound Vib., 322(4-5), 718-739. https://doi.org/10.1016/j.jsv.2008.11.028
  46. Yarnold, M.T. and Moon, F.L. (2015), "Temperature-based structural health monitoring baseline for long-span bridges", Eng. Struct., 86, 157-167. https://doi.org/10.1016/j.engstruct.2014.12.042
  47. Zhang, H., Qi, X., Sun, X. and Fan, S. (2008), "Application of Hilbert-Huang transform to extract arrival time of ultrasonic lamb waves", Proceedings of 2008 International Conference on Audio, Language and Image Processing, pp. 1-4.
  48. Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P. and Gao, R.X. (2019), "Deep learning and its applications to machine health monitoring", Mech. Syst. Signal Process., 115, 213-237. https://doi.org/10.1016/j.ymssp.2018.05.050
  49. Zhou, H.F., Ni, Y.Q. and Ko, J.M. (2011), "Eliminating temperature effect in vibration-based structural damage detection", 137(12), 785-796. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000273
  50. Zhu, Y., Ni, Y.Q., Jin, H., Inaudi, D. and Laory, I. (2019), "A temperature-driven MPCA method for structural anomaly detection", Eng. Struct., 190, 447-458. https://doi.org/10.1016/j.engstruct.2019.04.004