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2023 | OriginalPaper | Chapter

5. Wavelet Energy Features for Damage Identification: Sensitivity to Measurement Uncertainties

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Abstract

In vibration-based structural parameter identification, wavelet transformation has been widely used for extraction of damage pertinent data for onward identification of structural parameters or the occurrence of anomalies. Among wavelet-based techniques, the use of wavelet packet node energy (WPNE) as damage-sensitive features has attracted much research interest in more recent years. WPNE features contain detailed information which can be highly sensitive to local damages. However, most of the existing studies in the literature on using wavelet energy-based features have been numerical and involved idealised assumptions such as perfect and identical excitations among different tests. This paper presents an investigation into the tolerance of a wavelet packet energy with neural network approach to uncertainties in the input excitations and measurement noises. WPNEs are extracted from vibration signals from impact tests as feature proxies and a back-propagation neural network is used for classification. The method is firstly applied on a beam model using finite element simulations, in which variation in the excitations and measurement noises are incorporated to investigate the susceptibility of the approach to such uncertainties. Subsequently, the method is applied to the experimental data from the laboratory test of a steel beam. The results from both the numerical simulations and the experimental verification demonstrate that the wavelet energy with neural network approach to detecting structural changes is workable, and given a reasonably controlled impact test, it is possible to identify the initiation of damage with good accuracy.

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Literature
1.
go back to reference Yen, G.G., Lin, K.C.: Wavelet packet feature extraction for vibration monitoring. IEEE Trans. Ind. Electron. 47(3), 650–667 (2000)CrossRef Yen, G.G., Lin, K.C.: Wavelet packet feature extraction for vibration monitoring. IEEE Trans. Ind. Electron. 47(3), 650–667 (2000)CrossRef
2.
go back to reference Han, J.G., Ren, W.X., Sun, Z.S.: Wavelet packet based damage identification of beam structures. Int. J. Solids Struct. 42(26), 6610–6627 (2005)CrossRef Han, J.G., Ren, W.X., Sun, Z.S.: Wavelet packet based damage identification of beam structures. Int. J. Solids Struct. 42(26), 6610–6627 (2005)CrossRef
3.
go back to reference Mikami, S., Beskhyroun, S., Oshima, T.: Wavelet packet based damage detection in beam-like structures without baseline modal parameters. Struct. Infrastruct. Eng. 7(3), 211–227 (2011)CrossRef Mikami, S., Beskhyroun, S., Oshima, T.: Wavelet packet based damage detection in beam-like structures without baseline modal parameters. Struct. Infrastruct. Eng. 7(3), 211–227 (2011)CrossRef
4.
go back to reference Sun, Z., Chang, C.C.: Structural damage assessment based on wavelet packet transform. J. Struct. Eng. ASCE. 128(10), 1354–1361 (2002)CrossRef Sun, Z., Chang, C.C.: Structural damage assessment based on wavelet packet transform. J. Struct. Eng. ASCE. 128(10), 1354–1361 (2002)CrossRef
5.
go back to reference Graps, A.: An introduction to wavelets. IEEE Comput. Sci. Eng. 2(2), 50–61 (1995)CrossRef Graps, A.: An introduction to wavelets. IEEE Comput. Sci. Eng. 2(2), 50–61 (1995)CrossRef
6.
go back to reference Coifman, R.R., Wickerhauser, M.V.: Entropy-based algorithms for best basis selection. IEEE Trans. Inf. Theory. 38(2), 713–718 (1992)CrossRef Coifman, R.R., Wickerhauser, M.V.: Entropy-based algorithms for best basis selection. IEEE Trans. Inf. Theory. 38(2), 713–718 (1992)CrossRef
7.
go back to reference Mallat, S., Peyre, G.: A wavelet tour of signal processing the sparse way preface to the sparse edition. In: Wavelet tour of signal processing: the Sparse Way, 3rd edn. Elsevier (2009)MATH Mallat, S., Peyre, G.: A wavelet tour of signal processing the sparse way preface to the sparse edition. In: Wavelet tour of signal processing: the Sparse Way, 3rd edn. Elsevier (2009)MATH
8.
go back to reference Liu, Y.-Y., Ju, Y.-F., Duan, C.-D., Zhao, X.-F.: Structure damage diagnosis using neural network and feature fusion. Eng. Appl. Artif. Intell. 24, 87–92 (2011)., 2010.08.011CrossRef Liu, Y.-Y., Ju, Y.-F., Duan, C.-D., Zhao, X.-F.: Structure damage diagnosis using neural network and feature fusion. Eng. Appl. Artif. Intell. 24, 87–92 (2011)., 2010.08.011CrossRef
9.
go back to reference Ghiasi, R., Torkzadeh, P., Noori, M.: A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function. Struct. Health Monit. 15(3), 302–316 (2016)CrossRef Ghiasi, R., Torkzadeh, P., Noori, M.: A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function. Struct. Health Monit. 15(3), 302–316 (2016)CrossRef
10.
go back to reference Cao, M., Ding, Y., Ren, W., Wang, Q., Ragulskis, M., Ding, Z.: Hierarchical wavelet-aided neural intelligent identification of structural damage in Noisy conditions. Appl. Sci. 7(4), 391 (2017). 1–20CrossRef Cao, M., Ding, Y., Ren, W., Wang, Q., Ragulskis, M., Ding, Z.: Hierarchical wavelet-aided neural intelligent identification of structural damage in Noisy conditions. Appl. Sci. 7(4), 391 (2017). 1–20CrossRef
Metadata
Title
Wavelet Energy Features for Damage Identification: Sensitivity to Measurement Uncertainties
Authors
Xiaobang Zhang
Yong Lu
Copyright Year
2023
DOI
https://doi.org/10.1007/978-3-031-04090-0_5