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. 2024 May 6;24(9):2950.
doi: 10.3390/s24092950.

Damage Severity Assessment of Multi-Layer Complex Structures Based on a Damage Information Extraction Method with Ladder Feature Mining

Jiajie Tu  1 , Jiajia Yan  1 , Xiaojin Ji  1 , Qijian Liu  1 , Xinlin Qing  1
Affiliations

Damage Severity Assessment of Multi-Layer Complex Structures Based on a Damage Information Extraction Method with Ladder Feature Mining

Jiajie Tu et al. Sensors (Basel). .

Abstract

Multi-layer complex structures are widely used in large-scale engineering structures because of their diverse combinations of properties and excellent overall performance. However, multi-layer complex structures are prone to interlaminar debonding damage during use. Therefore, it is necessary to monitor debonding damage in engineering applications to determine structural integrity. In this paper, a damage information extraction method with ladder feature mining for Lamb waves is proposed. The method is able to optimize and screen effective damage information through ladder-type damage extraction. It is suitable for evaluating the severity of debonding damage in aluminum-foamed silicone rubber, a novel multi-layer complex structure. The proposed method contains ladder feature mining stages of damage information selection and damage feature fusion, realizing a multi-level damage information extraction process from coarse to fine. The results show that the accuracy of damage severity assessment by the damage information extraction method with ladder feature mining is improved by more than 5% compared to other methods. The effectiveness and accuracy of the method in assessing the damage severity of multi-layer complex structures are demonstrated, providing a new perspective and solution for damage monitoring of multi-layer complex structures.

Keywords: damage information extraction; damage severity assessment; ladder feature mining; multi-layer complex structure.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Lamb wave propagation. (a) Schematic diagram of Lamb wave excitation–reception. (b) Formation of Lamb in a free plate.
Figure 2
Figure 2
Lamb wave modes. (a) S mode; (b) A mode.
Figure 3
Figure 3
The flowchart of the experiment.
Figure 4
Figure 4
Multi-layer complex material preparation process.
Figure 5
Figure 5
Multi-channel damage monitoring system.
Figure 6
Figure 6
The sensor network layout of the distance test.
Figure 7
Figure 7
Structural damage dimensions and location diagram.
Figure 8
Figure 8
Comparison of the received signals of the multi-layer complex structure at different sensing distances.
Figure 9
Figure 9
Comparison of signals without damage and signals with different damage lengths. (a) Baseline signals and damage signals. (b) Undamaged and damaged scattered signals.
Figure 10
Figure 10
Hilbert transform of baseline, damage and scattered signals of different damage lengths. (a) Damage length: 1 cm. (b) Damage length: 3 cm. (c) Damage length: 5 cm. (d) Damage length: 7 cm. (e) Damage length: 9 cm. (f) Damage length: 11 cm. (g) Damage length: 13 cm. (h) Damage length: 15 cm.
Figure 10
Figure 10
Hilbert transform of baseline, damage and scattered signals of different damage lengths. (a) Damage length: 1 cm. (b) Damage length: 3 cm. (c) Damage length: 5 cm. (d) Damage length: 7 cm. (e) Damage length: 9 cm. (f) Damage length: 11 cm. (g) Damage length: 13 cm. (h) Damage length: 15 cm.
Figure 11
Figure 11
The correlation coefficient of the acquired signal and the maximum of the scattered signal at the excitation frequency of 30–300 kHz. (a) Correlation coefficient. (b) Maximum.
Figure 12
Figure 12
The RMS and VAR of the scattered signal at the excitation frequency of 70–90 kHz. (a) Root mean square. (b) Variance.
Figure 13
Figure 13
Spectrum and time frequency diagrams of scattered signals: (a) 70 kHz; (b) 75 kHz; (c) 80 kHz; (d) 85 kHz; (e) 90 kHz.
Figure 14
Figure 14
Normalized features. (a) Features 1–5. (b) Features 6–9. (c) Features 10–12. (d) Features 13–16.
Figure 15
Figure 15
Importance of individual features.
Figure 16
Figure 16
Different damage severities based on dual feature fusion.
Figure 17
Figure 17
The damage severity identification results of different methods by ten iterations of ten-fold cross-validation.
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