Skip to main content

Damage identification using the PZT impedance signals and residual learning algorithm

Abstract

Damage identification techniques are of essential importance to promote the efficiency, reliability and safety of any structural system. In recent years, many artificial intelligence (AI)-based approaches have been successfully applied to establish damage identification tools using sample structural responses. However, it is generally difficult to fully train a deep neural network, therefore, researchers usually use shallow neural networks, which is limited in terms of performance. Addressing these issues, this paper proposes a novel structural damage identification method based on the raw time-series structural response signals and a deep residual network (DRN). A deep residual network is designed for extracting features of the raw time-domain impedance responses signals that measured from steel beam under different damage conditions. In order to optimize the network’s performance, a residual learning algorithm and the Bayesian optimization algorithm are proposed and implemented. The results show that different structural conditions have been identified accurately. Also, the proposed methodology is suitable for processing structural responses signal with variable sequential length. Reasonable knowledge is required in damage detection and signal processing, which increases the applicability of the established method. Thus, the introduced method offers significant improvement for structural health monitoring (SHM) in terms of different damage sizes and location detection. To the best of our knowledge, this is the first work adopting DRN simultaneously on SHM non-image datasets of electro-mechanical impedance (EMI) signals.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

References

  1. 1.

    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):22–35

    Article  Google Scholar 

  2. 2.

    Rafiei MH, Adeli H (2017) A novel machine learning-based algorithm to detect damage in high-rise building structures. Struct Des Tall Spec Build 26(18):1–11

    Article  Google Scholar 

  3. 3.

    Tondini N, Bursi OS, Bonelli A, Fassin M (2015) Capabilities of a Fiber Bragg Grating sensor system to monitor the inelastic response of concrete sections in new tunnel linings subjected to earthquake loading. Comput-Aided Civ Inf Eng 30(8):636–653

    Article  Google Scholar 

  4. 4.

    Cho S, Spencer BF (2015) Sensor attitude correction of wireless sensor network for acceleration-based monitoring of civil structures. Comput-Aided Civ Infrastruct Eng 30(11):859–871

    Article  Google Scholar 

  5. 5.

    Wang D, Chen Z, Xiang W, Zhu H (2017) Experimental investigation of damage identification in beam structures based on the strain statistical moment. Adv Struct Eng 20(5):747–758

    Article  Google Scholar 

  6. 6.

    Wang D, Xiang W, Zhu H (2014) Damage identification in beam type structures based on statistical moment using a two step method. J Sound Vib 333(3):745–760

    Article  Google Scholar 

  7. 7.

    Kim BH, Stubbs N, Park T (2005) A new method to extract modal parameters using output-only responses. J Sound Vib 282(1–2):215–230

    Article  Google Scholar 

  8. 8.

    Cha YJ, Buyukozturk O (2015) Structural damage detection using modal strain energy and hybrid multiobjective optimization. Comput-Aid Civ Infrastruct Eng 30(5):347–358

    Article  Google Scholar 

  9. 9.

    Sun H, Feng D, Liu Y, Feng MQ (2015) Statistical regularization for identification of structural parameters and external loadings using state space models. Comput-Aid Civ Infrastruct Eng 30(11):843–858

    Article  Google Scholar 

  10. 10.

    Yuen KV, Mu HQ (2015) Real-time system identification: an algorithm for simultaneous model class selection and parametric identification. Comput-Aid Civ Infrastruct Eng 30(10):785–801

    Article  Google Scholar 

  11. 11.

    Bolourchi A, Masri SF, Aldraihem OJ (2015) Studies into computational intelligence and evolutionary approaches for model-free identification of hysteretic systems. Comput-Aid Civ Infrastruct Eng 30(5):330–346

    Article  Google Scholar 

  12. 12.

    Zhang J, Guo SL, Zhang QQ (2015) Mobile impact testing for structural flexibility identification with only a single reference. Comput-Aid Civ Infrastruct Eng 30(9):703–714

    Article  Google Scholar 

  13. 13.

    Lei Y, Zhou H, Lai ZL (2016) A computationally efficient algorithm for real-time tracking the abrupt stiffness degradations of structural elements. Comput-Aid Civ Infrastruct Eng 31(6):465–480

    Article  Google Scholar 

  14. 14.

    Cheng J, Xu RM, Tang XY, Sheng VS, Cai CT (2018) An abnormal network flow feature sequence prediction approach for DDoS attacks detection in big data environment. Comput Mater Contin 55(1):95–119

    Google Scholar 

  15. 15.

    Sanaz R, Armen DK (2012) A stochastic ground motion model with separable temporal and spectral nonstationarities. Earthq Eng Struct Dyn 41(11):1549–1568

    Article  Google Scholar 

  16. 16.

    Yan WJ, Ren WX (2012) Operational modal parameter identification from power spectrum density transmissibility. Comput-Aid Civ Infrastruct Eng 27(3):202–217

    MathSciNet  Article  Google Scholar 

  17. 17.

    Nair KK, Kiremidjian AS, Law KH (2006) Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure. J Sound Vib 291:349–368

    Article  Google Scholar 

  18. 18.

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

    Article  Google Scholar 

  19. 19.

    Park G, Cudney HH, Inman DJ (2000) An integrated health monitoring technique using structural impedance sensors. J Intell Mater Syst Struct 11(6):448–455

    Article  Google Scholar 

  20. 20.

    Xiang W, Wang D, Zhu H (2014) Damage identification in a plate structure based on strain statistical moment. Adv Struct Eng 17(11):1639–1655

    Article  Google Scholar 

  21. 21.

    Xu C, Yang Z, Qiao B et al (2019) Traveling distance estimation for dispersive Lamb waves through sparse Bayesian learning strategy. Smart Mater Struct 28(8):085008.

  22. 22.

    Zhao M, Zhou W, Huang Y et al (2020) Sparse Bayesian learning approach for propagation distance recognition and damage localization in plate-like structures using guided waves. Struct Health Monit 20(1):3–24

    Article  Google Scholar 

  23. 23.

    Tan Y, Zhang L (2019) Computational methodologies for optimal sensor placement in structural health monitoring: a review. Struct Heallth Monit 19(4):1287–1308

    Article  Google Scholar 

  24. 24.

    Zhang T, Biswal S, Wang Y (2020) SHMnet: condition assessment of bolted connection with beyond human-level performance. Struct Health Monit 19(4):1188–1201

    Article  Google Scholar 

  25. 25.

    Yan YJ, Yam LH, Cheng L, Yu L (2006) FEM modeling method of damage structures for structural damage detection. Compos Struct 72(2):193–199

    Article  Google Scholar 

  26. 26.

    LeCun Y, Bengio Y, Hinton GE (2015) Review: deep learning. Nature 521:436–444

    Article  Google Scholar 

  27. 27.

    Aljemely AH, Xuan J, Xu L, Jawad FKJ, Al-Azzawi O (2021) Wise-local response convolutional neural network based on Naïve Bayes theorem for rotating machinery fault classification. Appl Intell. https://doi.org/10.1007/s10489-021-02252-2

    Article  Google Scholar 

  28. 28.

    Abdeljaber O, Avci O, Kiranyaz S, Gabbouj M, Inman DJ (2017) Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J Sound Vib 388:154–170

    Article  Google Scholar 

  29. 29.

    Xia M, Li T, Xu L, Liu L, de Silva CW (2018) Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks. IEEE/ASME Trans Mechatron 23:101–110

    Article  Google Scholar 

  30. 30.

    Seventekidis P, Giagopoulos D, Arailopoulos A, Markogiannaki O (2020) Structural Health Monitoring using deep learning with optimal finite element model generated data. Mech Syst Signal Process 145:106972

    Article  Google Scholar 

  31. 31.

    Alazzawi O, Wang D (2021) Deep convolution neural network for damage identifications based on time-domain PZT impedance technique. J Mech Sci Technol 35(5):1809–1819

    Article  Google Scholar 

  32. 32.

    He K, Zhang X, Ren S, Sun J (2016) “Deep residual learning for image recognition”, in Proc. IEEE Comput Soc Conf Comput Vis Pattern Recognit, Las Vegas, , pp 770–778

    Google Scholar 

  33. 33.

    Ma S, Chu F, Han Q (2019) Deep residual learning with demodulated time-frequency features for fault diagnosis of planetary gearbox under nonstationary running conditions. Mech Syst Signal Process 127(1):190–201

    Article  Google Scholar 

  34. 34.

    Zhang W, Li X, Ding Q (2018) Deep residual learning-based fault diagnosis method for rotating machinery. ISA Trans 95:295–305

    Article  Google Scholar 

  35. 35.

    Zhao M, Kang M, Tang B, Pecht M (2018) Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes. IEEE Trans Ind Electron 65(5):4290–4300

    Article  Google Scholar 

  36. 36.

    Song Q, Yingqi W, Xueshi X, Lu Y, Min Y et al (2019) Real-time tunnel crack analysis system via deep learning. IEEE Access 7:64186–64197

    Article  Google Scholar 

  37. 37.

    Li R, Yuan Y, Zhang W, Yuan Y (2018) Unified vision-based methodology for simultaneous concrete defect detection and geolocalization. Comput Aid Civil Infrastruct Eng 33(7):527–544

    Article  Google Scholar 

  38. 38.

    Gao Y, Li K, Mosalam K, Gunay S (2018) Deep residual network with transfer learning for image-based structural damage recognition. Comput-Aid Civ Infrastruct Eng 33(17):748–768

    Article  Google Scholar 

  39. 39.

    Baptista FG, Filho JV (2009) A new impedance measurement system for PZT-based structural health monitoring. IEEE Trans Instrum Measurm 58(10):3602–3608

    Article  Google Scholar 

  40. 40.

    Günther J, Pilarski PM, Helfrich G, Shen H, Diepold K (2014) First steps towards an intelligent laser welding architecture using deep neural networks and reinforcement learning. Procedia Technol 15:474–483

    Article  Google Scholar 

  41. 41.

    Zhang W, Li X, Ding Q (2019) Deep residual learning-based fault diagnosis method for rotating machinery. ISA Trans 95(7):295–305

    Article  Google Scholar 

  42. 42.

    Brochu E, Cora VM, de Freitas N (2010) A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning [Online]. Available: http://arxiv.org/abs/1012.2599.

  43. 43.

    Grover J (2013) An introduction to Bayes’ Theorem and Bayesian Belief Networks (BBN). In: Strategic economic decision-making: using bayesian belief networks to solve complex problems, Springer, New York, pp 1–9

  44. 44.

    He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. Available: http://arxiv.org/abs/1502.01852

Download references

Acknowledgements

The authors acknowledge the editor and reviewers for their efforts. The authors also acknowledge the supports of the National Natural Science Fund of China (51278215) and the Basic Research Program of China (contract number: 2016YFC0802002).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Dansheng Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest to report regarding the present study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Alazzawi, O., Wang, D. Damage identification using the PZT impedance signals and residual learning algorithm. J Civil Struct Health Monit 11, 1225–1238 (2021). https://doi.org/10.1007/s13349-021-00505-9

Download citation

Keywords

  • Deep learning
  • Residual learning
  • Damage detection
  • Bayesian optimization
  • PZT sensor
  • Signals