Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Deep learning-based functional assessment of piezoelectric-based smart interface under various degradations

  • Nguyen, Thanh-Truong (Industrial Maintenance Training Center, Ho Chi Minh City University of Technology (HCMUT)) ;
  • Kim, Jeong-Tae (Department of Ocean Engineering, Pukyong National University) ;
  • Ta, Quoc-Bao (Department of Ocean Engineering, Pukyong National University) ;
  • Ho, Duc-Duy (Vietnam National University Ho Chi Minh City (VNU-HCM)) ;
  • Phan, Thi Tuong Vy (Center for Advanced Chemistry, Institute of Research and Development, Duy Tan University) ;
  • Huynh, Thanh-Canh (Center for Construction, Mechanics and Materials, Institute of Research and Development, Duy Tan University)
  • Received : 2020.05.22
  • Accepted : 2021.04.14
  • Published : 2021.07.25
⟨ Previous Next ⟩

Abstract

The piezoelectric-based smart interface technique has shown promising prospects for electro-mechanical impedance (EMI)-based damage detection with various successful applications. During the process of EMI monitoring and damage identification, the operational functionality of the smart interface device is a major concern. In this study, common functional degradations that occurred in the smart interface are diagnosed using a deep learning-based method. Firstly, the effect of functional degradations on the EMI responses is analytically discussed. Secondly, a critical structural joint is selected as the test structure from which EM measurement using the smart interface is conducted. Thirdly, a numerical model corresponding to the experimental model is established and updated to reproduce the measured EMI responses. By using the updated numerical model, the EMI responses of the smart interface under the common functional degradations, such as the shear lag effect, the adhesive debonding, the sensor breakage, and the interface detaching, are simulated; then, the functional degradation-induced EMI changes are characterized. Finally, a convolutional neural network (CNN)-based functional assessment method is newly proposed for the smart interface. The CNN can automatically extract and directly learn optimal features from the raw EMI signals without preprocessing. The CNN is trained and tested using the datasets obtained from the updated numerical model. The obtained results show that the proposed method was successful to classify four types of common defects in the smart interface, even under the effect of noises.

Keywords

Acknowledgement

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 107.01-2019.332.

References

  1. 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
  2. Ai, D., Luo, H. and Zhu, H. (2016), "Diagnosis and validation of damaged piezoelectric sensor in electromechanical impedance technique", J. Intell. Mater. Syst. Struct., 28(7), 837-850. https://doi.org/10.1177/1045389x16657427
  3. Annamdas, V.G.M., Radhika, M.A. and Yang, Y. (2009), "Easy installation method of piezoelectric (PZT) transducers for health monitoring of structures using electro-mechanical impedance technique", Proceedings of Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, (Volume 7292, pp. 729227-729221), San Diego, CA, USA, March.
  4. Azimi, M. and Pekcan, G. (2020), "Structural health monitoring using extremely compressed data through deep learning", Comput.-Aided Civil Infrastr. Eng., 35(6), 597-614. https://doi.org/10.1111/mice.12517
  5. Bhalla, S. and Moharana, S. (2012), "A refined shear lag model for adhesively bonded piezo-impedance transducers", J. Intell. Mater. Syst. Struct., 24(1), 33-48. https://doi.org/10.1177/1045389x12457837
  6. Dang, N.-L., Huynh, T.-C. and Kim, J.-T. (2019), "Local strandbreakage detection in multi-strand anchorage system using an impedance-based stress monitoring method-Feasibility study", Sensors, 19(5), 1054. https://doi.org/10.3390/s19051054
  7. Giurgiutiu, V., Zagrai, A. and Jing Bao, J. (2002), "Piezoelectric wafer embedded active sensors for aging aircraft structural health monitoring", Struct. Health Monitor., 1(1), 41-61. https://doi.org/10.1177/147592170200100104
  8. Gresil, M., Yu, L., Giurgiutiu, V. and Sutton, M. (2012), "Predictive modeling of electromechanical impedance spectroscopy for composite materials", Struct. Health Monitor., 11(6), 671-683. https://doi.org/10.1177/1475921712451954
  9. Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, L. and Wang, G. (2015), "Recent advances in convolutional neural networks", Pattern Recogn., 77, 354-377. https://doi.org/10.1016/j.patcog.2017.10.013
  10. Huynh, T.-C. (2020), "Structural parameter identification of a bolted connection embedded with a piezoelectric interface", Vietnam J. Mech., 1-16. https://doi.org/10.15625/0866-7136/14806
  11. Huynh, T.-C. (2021), "Vision-based autonomous bolt-looseness detection method for splice connections: Design, lab-scale evaluation, and field application", Automat. Constr., 124, 103591. https://doi.org/10.1016/j.autcon.2021.103591
  12. Huynh, T.-C. and Kim, J.-T. (2014), "Impedance-based cable force monitoring in tendon-anchorage using portable PZT-interface technique", Mathe. Problems Eng., 11, Article 784731. https://doi.org/10.1155/2014/784731
  13. Huynh, T.-C. and Kim, J.-T. (2017), "Quantitative damage identification in tendon anchorage via PZT interface-based impedance monitoring technique", Smart Struct. Syst., Int. J., 20(2), 181-195. https://doi.org/10.12989/sss.2017.20.2.181
  14. Huynh, T.C., Dang, N.L. and Kim, J.T. (2018), "Preload monitoring in bolted connection using piezoelectric-based smart interface", Sensors, 18(9), 2766. https://doi.org/10.3390/s18092766
  15. Huynh, T.-C., Lee, S.-Y., Dang, N.-L. and Kim, J.-T. (2019), "Sensing region characteristics of smart piezoelectric interface for damage monitoring in plate-like structures", Sensors, 19(6), 1377. https://doi.org/10.3390/s19061377
  16. Huynh, T.-C., Nguyen, T.-D., Ho, D.-D., Dang, N.-L. and Kim, J.-T. (2020), "Sensor Fault Diagnosis for Impedance Monitoring Using a Piezoelectric-Based Smart Interface Technique", Sensors, 20(2), 510. https://doi.org/10.3390/s20020510
  17. Jin, C. and Wang, X. (2011), "Analytical modelling of the electromechanical behaviour of surface-bonded piezoelectric actuators including the adhesive layer", Eng. Fract. Mech., 78(13), 2547-2562. https://doi.org/10.1016/j.engfracmech.2011.06.014
  18. Johnson, K.L. (1985), Contact Mechanics, Cambridge University Press. https://doi.org/DOI: 10.1017/CBO9781139171731
  19. Kim, J.-T., Park, J.-H., Hong, D.-S. and Park, W.-S. (2010), "Hybrid health monitoring of prestressed concrete girder bridges by sequential vibration-impedance approaches", Eng. Struct., 32(1), 115-128. https://doi.org/https://doi.org/10.1016/j.engstruct.2009.08.021
  20. Kim, H., Liu, X., Ahn, E., Shin, M., Shin, S.W. and Sim, S.-H. (2019), "Performance assessment method for crack repair in concrete using PZT-based electromechanical impedance technique", NDT & E Int., 104, 90-97. https://doi.org/10.1016/j.ndteint.2019.04.004
  21. LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W. and Jackel, L.D. (1989), "Backpropagation applied to handwritten zip code recognition", Neural Computat., 1(4), 541-551. https://doi.org/10.1162/neco.1989.1.4.541
  22. LeCun, Y., Bengio, Y. and Hinton, G. (2015), "Deep learning", Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
  23. Li, W., Liu, T., Zou, D., Wang, J. and Yi, T.-H. (2019), "PZT based smart corrosion coupon using electromechanical impedance", Mech. Syst. Signal Process., 129, 455-469. https://doi.org/https://doi.org/10.1016/j.ymssp.2019.04.049
  24. Liang, C., Sun, F.P. and Rogers, C.A. (1994), "Coupled electromechanical analysis of adaptive material systems-determination of the actuator power consumption and system energy transfer", J. Intell. Mater. Syst. Struct, 5(1), 12-20. https://doi.org/10.1177/1045389x9400500102
  25. Mansoor, M.B., Koble, S., Wong, T.W., Woias, P. and Goldschmidtboing, F. (2017), "Design, characterization and sensitivity analysis of a piezoelectric ceramic/metal composite transducer", Micromachines, 8(9), 271. https://www.mdpi.com/2072-666X/8/9/271 https://doi.org/10.3390/mi8090271
  26. Min, J., Park, S. and Yun, C.-B. (2010), "Impedance-based structural health monitoring using neural networks for autonomous frequency range selection", Smart Mater. Struct., 19(12), 125011. http://stacks.iop.org/0964-1726/19/i=12/a=125011 https://doi.org/10.1088/0964-1726/19/12/125011
  27. Nguyen, K.-D. and Kim, J.-T. (2012), "Smart PZT-interface for wireless impedance-based prestress-loss monitoring in tendon-anchorage connection", Smart Struct. Syst., Int. J., 9(6), 489-504. https://doi.org/10.12989/sss.2012.9.6.489
  28. Nguyen, K.-D., Lee, S.-Y., Lee, P.-Y. and Kim, J.-T. (2011), "Wireless SHM for bolted connections via multiple PZT-interfaces and Imote2-platformed impedance sensor node", Proceedings of the 6th International Workshop on Advanced Smart Materials and Smart Structures Technology (ANCRiSST2011), Dalian, China, July.
  29. Ong, C., Yang, Y., Wong, Y., Bhalla, S., Lu, Y. and Soh, C.K. (2003), "Effects of adhesive on the electromechanical response of a piezoceramic-transducer-coupled smart system", In: Smart Materials, Structures, and Systems, Vol. 5062, pp. 241-247.
  30. Park, G., Cudney, H.H. and Inman, D.J. (2001), "Feasibility of using impedance-based damage assessment for pipeline structures", Earthq. Eng. Struct. Dyn., 30(10), 1463-1474. https://doi.org/10.1002/eqe.72
  31. Park, G., Farrar, C.R., Rutherford, A.C. and Robertson, A.N. (2006), "Piezoelectric active sensor self-diagnostics using electrical admittance measurements", J. Vib. Acoust., 128(4), 469-478. https://doi.org/10.1115/1.2202157
  32. Park, S., Park, G., Yun, C.-B. and Farrar, C.R. (2008), "Sensor self-diagnosis using a modified impedance model for active sensing-based structural health monitoring", Struct. Health Monitor., 8(1), 71-82. https://doi.org/10.1177/1475921708094792
  33. Pasquier, R. and Smith, I.F.C. (2016), "Iterative structural identification framework for evaluation of existing structures", Eng. Struct., 106, 179-194. https://doi.org/https://doi.org/10.1016/j.engstruct.2015.09.039
  34. Ritdumrongkul, S., Abe, M., Fujino, Y. and Miyashita, T. (2004), "Quantitative health monitoring of bolted joints using a piezoceramic actuator-sensor", Smart Mater. Struct., 13(1), 20. http://stacks.iop.org/0964-1726/13/i=1/a=003 https://doi.org/10.1088/0964-1726/13/1/003
  35. Ryu, J.-Y., Huynh, T.-C. and Kim, J.-T. (2019), "Tension force estimation in axially loaded members using wearable piezoelectric interface technique", Sensors, 19(1), 47. https://doi.org/10.3390/s19010047
  36. Shih, H.W., Thambiratnam, D.P. and Chan, T.H.T. (2009), "Vibration based structural damage detection in flexural members using multi-criteria approach", J. Sound Vib., 323(3), 645-661. https://doi.org/https://doi.org/10.1016/j.jsv.2009.01.019
  37. Sirca, G.F. and Adeli, H. (2012), "System identification in structural engineering", Scientia Iranica, 19(6), 1355-1364. https://doi.org/https://doi.org/10.1016/j.scient.2012.09.002
  38. Song, G., Gu, H. and Mo, Y.-L. (2008), "Smart aggregates: multi-functional sensors for concrete structures-a tutorial and a review", Smart Mater. Struct., 17(3), 033001. https://doi.org/10.1088/0964-1726/17/3/033001
  39. Stubbs, N. and Kim, J.T. (1996), "Damage localization in structures without baseline modal parameters", AIAA J., 34(8), 1644-1649. https://doi.org/10.2514/3.13284
  40. Tawie, R., Park, H.B., Baek, J. and Na, W.S. (2019), "Damage detection performance of the electromechanical impedance (EMI) technique with various attachment methods on glass fibre composite plates", Sensors, 19(5), 1000. https://www.mdpi.com/1424-8220/19/5/1000 https://doi.org/10.3390/s19051000
  41. Uddin, M.N., Islam, M.S., Sampe, J., Ali, S.H.M. and Bhuyan, M.S. (2016), "Design and simulation of piezoelectric cantilever beam based on mechanical vibration for energy harvesting application", Proceedings of 2016 International Conference on Innovations in Science, Engineering and Technology (ICISET), Dhaka, Bangladesh, October.
  42. Xu, Y. and Liu, G. (2002), "A modified electro-mechanical impedance model of piezoelectric actuator-sensors for debonding detection of composite patches", J. Intell. Mater. Syst. Struct., 13(6), 389-396. https://doi.org/10.1177/104538902761696733
  43. Yan, S., Sun, W., Song, G., Gu, H., Huo, L.-S., Liu, B. and Zhang, Y.-G. (2009), "Health monitoring of reinforced concrete shear walls using smart aggregates", Smart Mater. Struct., 18(4), 047001. https://doi.org/10.1088/0964-1726/18/4/047001