A Novel Fault Diagnosis of a Rolling Bearing Method Based on Variational Mode Decomposition and an Artificial Neural Network

Round 1

Reviewer 1 Report

The paper is well written and well presented but requires minimal revision.

In lines 137-138, "the number of points has been reduced from 1024 to 128", this reduction of points merits further explanation.

In lines 288-289, "The optimal sample number is xx and the shape of its input data is 800x1024" what does xx mean?   it should be corrected if it is an error.

The sampling frequency has not been specified. Please specify the sampling period.

The effect of the sampling period has not been investigated. It is very interesting to study the effect of the sampling period on the accuracy of the method.

Also, it is very interesting to discuss the possibility of an embedded implementation of this approach

Author Response

Please see the attachment. Thank you very much!

Author Response File: Author Response.docx

Reviewer 2 Report

1) The originality of the paper is poor; in fact, the approaches are well known, and many related works can be found in the literature. The motivation for the work is not clear.

2) We suggest the authors optimize the size and quality of the figures.

3) The authors must make a better effort at referencing the significant papers. There are several papers published in MDP dealing with fault diagnosis and ANN that are not cited.

4) There are many symbols and abbreviations. A list should be given. 

5) What is the conclusion at the end of Section 1 (Introduction)? We suggest to the authors that they add a paragraph in which they explain the advantages of the proposed contribution (regarding the presented works).

6) We suggest the author give more details about the details of the experiments (the acquisition chain, test bed, etc.).

7) We suggest that the authors include more details about the optimization of ANN structural parameters.

8) The relative works in the following references can be mentioned in the introduction:

-    Samanta, B., Al-Balushi, K., Al-Araimi, S.: Artificial neural networks and genetic algorithm for bearing fault detection. Soft Comput 10 (2006). DOI https://doi.org/10.1007/s00500-005-0481-0

 -  Kerboua, A. Metatla, R. Kelaiaia and M. Batouche, "Fault Diagnosis in Induction Motor using Pattern Recognition and Neural Networks," 2018 International Conference on Signal, Image, Vision and their Applications (SIVA), Guelma, Algeria, 2018, pp. 1-7, doi: 10.1109/SIVA.2018.8660995.

Author Response

Please see the attachment. Thank you very much!

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

Thanks for the point-by-point responses. The authors have addressed all reviewer comments with justifications, and the manuscript quality has improved as a result of the revised version.

One minor question: What is the significance of Figure 15? Comparison should always be done when different authors use the same dataset. Further classification accuracy also depends on the type of dataset used, and it varies based on the type of dataset.

Author Response

Please see the attachment. Thank you very much!

Author Response File: Author Response.pdf