Next Article in Journal
Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder
Previous Article in Journal
An Interface-Corrected Diffuse Interface Model for Incompressible Multiphase Flows with Large Density Ratios
 
 
Article
Peer-Review Record

Research on Wind Turbine Blade Surface Damage Identification Based on Improved Convolution Neural Network

Appl. Sci. 2022, 12(18), 9338; https://doi.org/10.3390/app12189338
by Li Zou 1,2,3,* and Haowen Cheng 1,2
Appl. Sci. 2022, 12(18), 9338; https://doi.org/10.3390/app12189338
Submission received: 24 August 2022 / Revised: 13 September 2022 / Accepted: 14 September 2022 / Published: 18 September 2022

Round 1

Reviewer 1 Report

please refer to the attached file 

Comments for author File: Comments.pdf

Author Response

Please attached find the response to reviewers. Hope the revision will meet with your approval. Thanks.

Author Response File: Author Response.pdf

Reviewer 2 Report

The manuscript is well structured and well argued. However, several rectifications and modifications are required to ensure that its quality stands up to this reputed journal.  

1.       The authors have proposed an improved convolution neural network model for the identification of damage features of wind turbine blades.

2.       The English language must be improved. There are several grammatical errors as one goes through the manuscript that requires rectification. Most of the sentences convey no proper meaning and could be off-putting to the readers and practitioners.

3.       The first section introduces a basic outlook on related works of damage identification of wind turbine blade and a brief literature review of various methodologies used for identification of damage features of wind turbine is presented.

4.       The resolutions of all the figures of manuscript are required to be improved.

5.       Authors have to clearly mention the details of why the training set and test set are divided in a 9:1 ratio?

6.       What are the criteria for selecting attenuation rate parameters of Adam gradient descent optimizer?

7.       Suggested to compare the performance of proposed enhanced CNN with recently reported methods for the damage identification of wind turbine features with respect to the accuracy and recall

8.         To keep things fair, a brief discussion of the demerits with the proposed analysis should be provided.

 

9.       The manuscript has the potential to be improved and requires minor rectifications. With that being said, I wish the authors all the best on their endeavor to improve the quality of the manuscript.

Author Response

Please see the attachment response to reviewers. Hope our revision could meet with your approval. Thanks.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The authors addressed all of the reviewer's concerns 

Back to TopTop