Damage Severity Assessment of Multi-Layer Complex Structures Based on a Damage Information Extraction Method with Ladder Feature Mining
Abstract
:1. Introduction
2. Lamb Wave Propagation Theory in Plate Structures
3. Method
3.1. Damage Information Selection Stage
- According to the propagation law and propagation characteristics of the Lamb wave in the multi-layer complex structure, a suitable monitoring frequency range A0–A1 is selected.
- When the multi-layer complex structure is damaged to different degrees, we compare the damage signals with the baseline signals under the monitoring frequency range A0–A1. The time domain feature information is analyzed after extracting the time domain features, such as the correlation coefficient, maximum value, variance and root mean square. The frequency selection range is gradually narrowed down, and, finally, a most sensitive frequency band B0–B1 is determined. In this experiment, the selected optimal frequency band difference is 20 kHz.
- Within the monitoring frequency of B0–B1, the frequency domain analysis based on Fast Fourier Transform (FFT) is carried out on the scattered signals of different degrees. Through the peak analysis of the spectrum, a common sensitive frequency C0 is obtained, which is the optimal damage monitoring frequency.
3.2. Damage Feature Fusion Stage
3.2.1. Multi-Dimensional Feature Extraction
3.2.2. Dual Feature Fusion
- Obtain the value of the best decision tree. The feature matrix XNEW and the target variable matrix y are separated. Different numbers of decision trees are iterated by setting the seed of the random number generator. Each run evaluates the MSE and selects the number of decision trees Nt that minimizes the MSE. The formula is as follows:
- 2.
- Obtain feature importance. Construct the RF model using the optimal number of decision trees. The feature importance scores are obtained by enabling the calculation of feature importance for out-of-bag (OOB) sample prediction errors. The importance of features φ can be expressed as follows:
- 3.
- Feature selection: The features are ranked according to their importance. The top 50% of features in terms of importance are filtered from highest to lowest to complete the first damage feature fusion. The feature set obtained from this feature dimension optimization process is XNEW’.
4. Experimental Program
4.1. Multi-Layer Complex Structure Preparation
4.2. Sensor Signal Test
4.3. Verification of Damage Monitoring Ability
5. Results and Discussion
5.1. Guided Wave Signal Analysis of Multi-Layer Complex Structure
5.1.1. Structural Signal Testing and Analysis
5.1.2. Damage Signal Processing
5.2. Dual Damage Information Selection Based on Time and Frequency Domains
5.3. Dual Damage Feature Fusion Based on Dimension Optimization and Dimension Fusion
5.4. Damage Severity Identification Results
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Feature Number | Parameter | Calculation Equation | |
---|---|---|---|
1 | Correlation coefficient | (5) | |
2 | Maximum value | (6) | |
3 | Peak-to-peak value | (7) | |
4 | Root mean square | (8) | |
5 | Variance | (9) | |
6 | Peak of the first wave | (10) | |
7 | Flight time of the first wave | (11) | |
8 | Peak of the second wave | (12) | |
9 | Flight time of the second wave | (13) | |
10 | SDT | (14) | |
11 | SST | (15) | |
12 | Envelope energy | (16) |
Feature Number | Parameter | Calculation Equation | |
---|---|---|---|
13 | Maximum value | (17) | |
14 | Energy | (18) | |
15 | SDS | (19) | |
16 | SSS | (20) |
Method | Advantages | Limitations |
---|---|---|
RF feature dimension optimization | Robustness Ability to handle non-linear relationships | Easily overfitted Large time cost |
PCA feature dimension fusion | Computationally efficient Highly interpretable | Easy to lose information Difficulty in capturing nonlinear relationships |
RF + PCA dual feature fusion | Strong model generalization ability Low risk of overfitting Applicable to small sample data Low time cost | Some information may be lost |
Technical Parameter | Value |
---|---|
Excitation frequency range | 10–1000 kHz |
Conversion rates | 48 MHz |
Output voltage range | Min: ±10 V; Max: ±60 V |
Memory | 32,000 Samples |
Sampling rates | 6, 12, 24, 48 MHz/s |
Resolution | 12-bit |
ADC range | ±1 V |
Adjustment range | 10–40 dB, step: 1 dB |
Date | Day 1 | Day 3 | Day 5 | Day 10 | Day 15 |
---|---|---|---|---|---|
Correlation index (Ci) | 99.87% | 98.62% | 97.97% | 96.45% | 96.40% |
Model | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|
Non-damage | 3 | 1 | 2 | 1 | 4 | 2 | 3 | 2 | 1 | 5 |
Damage severity 1 | 6 | 8 | 10 | 8 | 10 | 7 | 8 | 10 | 6 | 10 |
Damage severity 2 | 14 | 15 | 14 | 12 | 11 | 12 | 13 | 13 | 11 | 15 |
Damage severity 3 | 18 | 19 | 20 | 17 | 20 | 16 | 16 | 19 | 18 | 20 |
Methods | Accuracy |
---|---|
Normalization | 83.75% |
Normalization + Dimension optimization | 89.00% |
Normalization + Dimension fusion | 90.25% |
Normalization + Dual feature fusion | 96.00% |
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Share and Cite
Tu, J.; Yan, J.; Ji, X.; Liu, Q.; Qing, X. Damage Severity Assessment of Multi-Layer Complex Structures Based on a Damage Information Extraction Method with Ladder Feature Mining. Sensors 2024, 24, 2950. https://doi.org/10.3390/s24092950
Tu J, Yan J, Ji X, Liu Q, Qing X. Damage Severity Assessment of Multi-Layer Complex Structures Based on a Damage Information Extraction Method with Ladder Feature Mining. Sensors. 2024; 24(9):2950. https://doi.org/10.3390/s24092950
Chicago/Turabian StyleTu, Jiajie, Jiajia Yan, **ao** Ji, Qijian Liu, and **nlin Qing. 2024. "Damage Severity Assessment of Multi-Layer Complex Structures Based on a Damage Information Extraction Method with Ladder Feature Mining" Sensors 24, no. 9: 2950. https://doi.org/10.3390/s24092950