Probability-Based Diagnostic Imaging of Fatigue Damage in Carbon Fiber Composites Using Sparse Representation of Lamb Waves

Round 1

Reviewer 1 Report

1. As for novelty, the author proposed a damage factor based on the sparse representation of the Lamb wave signal for carbon fiber Probabilistic Imaging of Fatigue Damage in Composite Materials. The idea is good, however, there are some similar studies. Some example papers are listed as follows, and you may find more.

a)    Wang, W., Bao, Y., Zhou, W., & Li, H. (2018). Sparse representation for Lamb-wave-based damage detection using a dictionary algorithm. Ultrasonics, 87, 48-58.

b)    Eybpoosh, M., Berges, M., & Noh, H. Y. (2017). An energy-based sparse representation of ultrasonic guided-waves for online damage detection of pipelines under varying environmental and operational conditions. Mechanical Systems and Signal Processing, 82, 260-278.

c)    Z Zhang, H., Lin, J., Hua, J., & Tong, T. (2022). Interpretable convolutional sparse coding method of Lamb waves for damage identification and localization. Structural Health Monitoring, 21(4), 1790-1804.

d)    …

 

In the introduction, the authors should clarify the difference and their own novelties compared to similar studies. Also, comparison of computational time and accuracy with these cutting-edge methods under the same conditions should be presented.

 

2. Line 21, “Represent” should be changed to “represent”.

 

3. Line 223, Space is required between unit and number, “Its size is 15.24cm×25.4cm”.

 

4. Line 220. Section 4.1 is too brief, and more details should be added, such as (1) Model of test equipment, (2) How test equipment was operated (auto or manual), (3) Load time, etc.

 

5. Line 375. The author needs to explain the selection principle of SNR.

 

6. Authors should look into newer and more powerful/sophisticate damage detection algorithms as part of their future extension of the research such as supervised machine learning, and include a comment to that effect in the Conclusion or Future work.

[1] Chu, H., Wang, W., & Deng, L. (2022). Tiny‐Crack‐Net: A multiscale feature fusion network with attention mechanisms for segmentation of tiny cracks. Computer‐Aided Civil and Infrastructure Engineering, 37(14), 1914-1931.

[2] Sampath, S., Jang, J., & Sohn, H. (2022). Ultrasonic Lamb wave mixing based fatigue crack detection using a deep learning model and higher-order spectral analysis. International Journal of Fatigue, 163, 107028.

[3] Wu, J., Xu, X., Liu, C., Deng, C., & Shao, X. (2021). Lamb wave-based damage detection of composite structures using deep convolutional neural network and continuous wavelet transform. Composite Structures, 276, 114590.

Author Response

Dear reviewer, thank you very much for giving us an opportunity to revise the manuscript. We have responded to your comments. Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Review of the manuscript:

Probability-based diagnostic imaging of fatigue damage in carbon fiber composites based on sparse representation of Lamb waves

1. The findings are sufficiently novel to warrant publication.

2. The conclusions are adequately supported by the data presented.

3. The article is clearly and logically written so that it can be understood by one who is not an expert in the specific field. The work provides an important contribution to its field, consistent with the scope of the journal.

The paper is describing the actual problematics. Therefore, this paper proposes a fatigue damage probability imaging method for carbon fiber composite materials based on the sparse representation of Lamb wave signals. Based on constructing the Lamb wave dictionary, a fast block sparse Bayesian learning algorithm is used to represent the Lamb wave signals sparsely, and the definition of Lamb wave sparse Represent the damage factor calculates the damage probability of the monitoring area and then image the fatigue damage of carbon fiber composite materials. The results show that the proposed damage factor can clearly distinguish the damaged area from the undamaged area and has strong noise immunity. Compared with the energy damage factor and the cross-correlation damage factor, the error percentages are reduced by at least 58.63%, 26 28.11%, and 8.43% for signal-to-noise ratios of 6 dB, 3 dB, and 0.1 dB after adding noise to signal, respectively. The results can more accurately reflect the real location and area of fatigue damage in carbon fiber composites.

Comments:

 

Row 65: Please explain abbreviations EDM and IMF

Row 97: Please explain what epsilon in the eq. 1 is, or correct E on the row102

Row 224: Please describe parameters of the testing machine, producer, type, country of producer.

 Row 227: Please describe the parameters of the piezoelectric sensors

Row 243: Eq.13. Please introduce the authors of Eq.

Row 303 and 311: Please describe the calculation and the creation  of the images on the Figs.12,13,14,15,16 and 17 ,(software, parameters…)

Comments for author File: Comments.pdf

Author Response

Dear reviewer, thank you very much for giving us an opportunity to revise the manuscript. We have responded to your comments. Please see the attachment.

Author Response File: Author Response.pdf