Fault Diagnosis Method of Bearings Based on SCSSA-VMD-MCKD

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This work is certainly interesting and has important potential consequences. But the manuscript needs the following modifications:

1.      Page 3, Line 99: Authors are recommended to revise the text “… mathematical theory and modal mixingLet the original[19]. Let the original signal …” to be specific and clearer and to avoid the repetition with the next phrase.

2.      Page 4, Line 142 to 146: Authors are recommended to add equation number.

3.      Page 5, Lines 161-162: Reviewer thinks there is an equation citing error, since the citation was for equation (7) and the discussion was about equation (8). Additionally, authors are recommended to renumber all the following equations.

4.      Page 9, Line 260: Authors are recommended to revise the caption text of Fig. 3.

5.      Page 10, Fig. 6: Authors are recommended to rise the upper limit of the vertical axis to avoid the SCSSA curve coincidence with the figure border.

Page 14, Line 362: Authors are recommended to revise the mistyping in the text of “… while the envelope spectrum aftdeconvolution of the …”.

Comments on the Quality of English Language

Listed in comments to authors.

Author Response

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Reviewer 2 Report

Comments and Suggestions for Authors

The following are some comments on the article to scientifically improve the paper:

1. Please provide a reference of Equation (15) if the Authors did not derive the Equations.

2. How did the authors determine all the parameters and variables in Equation (15)?

3. Please delete \ in the y-label in Figures 3-5.

4. VMD works based on the signal decomposition approach. It would be better if the proposed method is compared to other decomposition methods such as:

  - Empirical Mode Decomposition (EMD):

    "Why EMD and similar decompositions are of little benefit for bearing diagnostics"

  - Ensemble Empirical Mode Decomposition (EEMD):

    "Condition monitoring of naturally damaged slow speed slewing bearing based on ensemble empirical mode decomposition"

 

Author Response

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Reviewer 3 Report

Comments and Suggestions for Authors

 

Following are the suggestions for further clarity of the manuscript.

1.      It is recommended to have preamble for “heading 2.” in order to introduce its subheading of 2.1 and 2.2

2.      The guideline to determine the tuning coefficient and weighting factor is required to be included in the manuscript.

3.      Since the nature of swarm optimization does not guarantee in acquiring similar optimal solution, hence how does the authors ensure that the consistency of the solution? Perhaps statistical investigation for certain number of runs would be good to ensure the consistency of the solution which can be gauged through the mean value and standard deviation.

4.      The details on the setup parameter for SCSSA, SSA and PSO including their heuristics parameter setup are required to be included and explain in the manuscript.

5.      The number of iterations used to measure the solution speed may not accurately represent the solution speed. Perhaps, the “tic and toc” function can be employed to estimate the real time needed for the program to complete. Besides, the provided solution was only based upon one sample of solution, therefor, a certain number of runs is required to investigate the consistency of the proposed method.

6.      It is recommended to include quantitative outcome that measure the effectiveness of the proposed method. These quantitative outcomes can then be included in the abstract and conclusion sections

Comments on the Quality of English Language

understandable

Author Response

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Reviewer 4 Report

Comments and Suggestions for Authors

Reviewer comments:

 The paper will be ready for publication after some corrections that authors have to accomplish.

·        The main topic of the paper addresses the problems represented by early bearing defects and the SCSSA-VMD-MCKD method is proposed which can optimize the variation mode decomposition (VMD) and maximum correlated kurtosis deconvolution (MCKD) by integrating sparrow search algorithm with Sine-Cosine and Cauchy mutations (SCSSA). By leveraging the strengths of VMD and MCKD in reducing noise and improving fault frequency, this approach aims to efficiently diagnose rolling bearing faults;

·        Placing a subtitle on one page and the text on the other page should be avoided (e.g. section 2, 2.1, 2.2);

·        Also the figure caption should be maintained on the same page with the figure (figure 3);

·        There are some large spaces on the page that can be avoided by correctly sizing some figures so that a better appearance of the work is obtained;

·        In the simulation analysis section, the method of performing the analysis should be detailed, including the initial data for which the presentation is a bit ambiguous;

·        Regarding the obtained results the real implications should be emphasized presented as quantitative data and the practical applications for the real impact.

Author Response

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Reviewer 5 Report

Comments and Suggestions for Authors

Review, 24 June, 2024

Article Fault Diagnosis Method Of Bearings Based On SCSSA-VMD- 2MCKD

Dear Authors,

Thank you for your research, in my opinion there are important for industrial development. As your reviewer I have few notes:

Note 1. Why do you use the aberv. SCSSA-VMD-MCKD? My doubt stems from the fact that in line 9 you write: This  method optimizes the Variational Mode Decomposition (VMD) and Maximum Correlated Kurtosis Deconvolution (MCKD) by integrating the Sine-Cosine and Cauchy Mutation Sparrow Search Algorithm (SCSSA). In this order it should be VDM-MCKD-SCSSA. If you want to use the abbreviation SCSSA-VMD-MCKD, you have to change this sentence because further sentences show that SCSSA is the first in your methodology.

Note 2: Introduction. After lines from 71 to 81 , you have to write about your research. In this place should be your method: SCSSA-VMD-MCKD, and RQs (research questions).  You can also state, or even should, the purpose of your research, and perhaps formulate a research hypothesis.

Note 3: Section 2: Why do you start description of the basic principles from VMD, nest is MCKD, and last is SCSSA. I see that the SCSSA algorithm is background (bone of your method) but the Reader don’t have such knowledge. So write more arguments because in paper topic is: SCSSA-VMD-MCKD.

Note 4: Topic of section 2:, now is: 2. Basic Principles. In my opinion this topic must be have more details. My proposal: Description of methods: SCSSA-VMD-MCKD, or Presentation of used methods  or simply : Methods of research (analysis).

Note 5: Topic of section 3. Now is: 3. The fault diagnosis process.  My proposal: Optimization of SCSSA VMD and MCKD Processes or  Methodology of analysis: SCSSA-VMD-MCKD.

Note 6: Table 1. The values of each component Ec after VMD decomposition. Ec  should be Italic.

Note 7: Line 298 and next, why [….] : With a sampling frequency of 12.8 kHz, the optimized parameters were determined to be [644, 96, 1] for use in the MCKD algorithm, and what mean numbers: 644, 96 and 1.

Note 8: Line 374 and next: The optimization of MCKD using the SCSSA method effectively enhances the  impulse components in the signal, thereby achieving the goal of fault diagnosis

Why: the optimization of MCKD using the SCSSA method effectively enhances … why and by what, and where is VDM.

You have to write more arguments for optimization of your method process or research process.

 

Note 9: Conclusion. In my opinion,  the section topic should be: Conclusions from the research.

Note 10. A discussion section should be added to show the place of your research in the development of science and industrial improvements.

Note 11. The summary should be rewritten, and given the limitations of research and directions of further research.

Best wishes

 Reviewer

Author Response

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Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

1. The iThenticate result of this paper is 34% which is considered a high similarity index. Please reduce it to below 25%.

2. I did not find any reference related to the EEMD method. However, the authors used the EEMD method in the paper.

Author Response

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