Industrial Metaverse and Technical Diagnosis of Electric Drive Systems

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

>I think, a space is necessary (line 21).
>The ‘physical space’ needs additional details (figure 1).
>The final part of the first section should contain a short description of the paper organization.
>The introduction should present other hardware solutions applied for diagnostic systems of electric drives.
>A short description of diagnostic process of electric machines (a main stages) should be mentioned in the first section.
>The AI tools are often (even in the manuscript) used for (mainly) analysis of faults symptoms. It needs to be described in the introduction.
>The novelty of the work should be highlighted.
>The functions need additional descriptions (table 1).
>'gen-
erative artificial intelligence (GenAI) has been used in this paper'
It needs to be explained.
>The quality of the figure 3 should be improved.
>'To implement each scenario, functional blocks were connected in
the configuration field and launched for execution.'
Have you prepared original modules for the system? 
>The parameters of the laboratory drive are necessary (figure 2).
>The axis of the figures are not described (e.g. figure 6-8).
>Additional description of the information included in the figure 8 should be presented.
>'A MATLAB model was
launched in real time as a digital twin of the engine, to which parameters were transmitted'
How was the system configured? How was the digital twin obtained?
>How were the parameters of the Rocket Classifier assumed (section 4)?
>Is the number of the samples enough for training the Machine Learning model (line 223-line 231)?
>How does the system operate under other input values (not included in the training) applied for the detection model?

Author Response

Dear reviewer,

We are sincere thankful to you for your valuable and helpful comments. We tried to have taken into account all comments and now we are submitting the revised version of our article. In addition to all comments, we would like to point out that human testing of the system does not comply with the ethical publication rules. Therefore, we are forced to remove the section on the NASA TSL test and correct the article in this part. Thank You.

With best regards.

Dr. Natalia Koteleva.

 

Comment 1: I think, a space is necessary (line 21).

Authors’ answer: We agree with this comment. (Line 21 was changed).

 

Comment 2: The ‘physical space’ needs additional details (figure 1).

Authors’ answer: Physical space it is combination of real-world objects. In this work a laboratory unit uses as the real-world object. Its detailed description is presented in Section 3 Experiments. This phrases was added in the article (Lines 118-120)

Comment 3: The final part of the first section should contain a short description of the paper organization.

Authors’ answer: The short description of the paper was added (Lines 101-107)

Comment 4: The introduction should present other hardware solutions applied for diagnostic systems of electric drives.

Authors’ answer: This information was added (Lines 43-47)

Comment 5: A short description of diagnostic process of electric machines (a main stages) should be mentioned in the first section

Authors’ answer: This information was added (Lines 40-43)

Comment 6: The AI tools are often (even in the manuscript) used for (mainly) analysis of faults symptoms. It needs to be described in the introduction.

Authors’ answer: This information was added (Lines 53-57)

 

Comment 7: The novelty of the work should be highlighted.

Authors’ answer: This information was added (Lines 310-313)

 

Comment 8: The functions need additional descriptions (table 1).

Authors’ answer: This information was added (table 1/Description)

Comment 9: 'generative artificial intelligence (GenAI) has been used in this paper' It needs to be explained.

Authors’ answer: When we wrote the article, we used the MDPI template. The lines previously located in lines 115-119 remained from this template. They read: "In this section, where applicable, authors are required to disclose details of how generative artificial intelligence (GenAI) has been used in this paper (e.g., to generate text, data, or graphics, or to assist in study design, data collection, analysis, or interpretation). The use of GenAI for superficial text editing (e.g., grammar, spelling, punctuation, and formatting) does not need to be declared." These lines have been removed. (The Line 142)

 

Comment 10: The quality of the figure 3 should be improved.

Authors’ answer: Figure 3 has been split into two parts (a and b), and the image has been enlarged. The quality has been improved. (Lines 159-162)

 

Comment 11:  'To implement each scenario, functional blocks were connected in the configuration field and launched for execution. Have you prepared original modules for the system?

Authors’ answer: Yes, the system and modules are original. They were developed by us personally. This software has been used in the experiment part.

 

Comment 12: The parameters of the laboratory drive are necessary (figure 2).

Authors’ answer: This information was added (Lines 149-152)

 

Comment 13: The axis of the figures are not described (e.g. figure 6-8).

Authors’ answer: Axis was added for figure 6-8, also 10-12, 14-16 and 18-20.

 

Comment 14: Additional description of the information included in the figure 8 should be presented.

Authors’ answer: This information was added (Lines 207-211)

 

Comment 15: A MATLAB model was launched in real time as a digital twin of the engine, to which parameters were transmitted' How was the system configured? How was the digital twin obtained?

Authors’ answer: This information was added (Lines 215-218)

 

 

Comment 16: How were the parameters of the Rocket Classifier assumed (section 4)?

Authors’ answer: This information was added (The Line 286)

 

Comment 17: Is the number of the samples enough for training the Machine Learning model (line 223-line 231)?

Authors’ answer: Since the data is clearly distinguishable and the accuracy is 1 on both the training and test sets, we consider the data to be sufficient.

 

Comment 18: How does the system operate under other input values (not included in the training) applied for the detection model?

Authors’ answer: This paper presents the results of the first stages of evaluating the effectiveness of the proposed solutions. The input data was not varied. While it is expected that other defects will be detected using the same method, it may be necessary to modify the AI model, its settings, training time, learning rate etc.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Please see the attached report.

Comments for author File: Comments.pdf

Author Response

Dear reviewer,

We are sincere thankful to you for your valuable and helpful comments. We tried to have taken into account all comments and now we are submitting the revised version of our article. In addition to all comments, we would like to point out that human testing of the system does not comply with the ethical publication rules. Therefore, we are forced to remove the section on the NASA TSL test and correct the article in this part. Thank You.

With best regards.

Dr. Natalia Koteleva.

 

Comment 1: The paper lacks a clear problem formulation and research gap. The concepts of  metaverse and electric drive diagnostics are presented in an interesting way but currently remain at a conceptual level without well-defined hypotheses or quantitative objectives.

Authors’ answer: We added information in the introduction part and try to change our article and make it better (Lines 37-57)

 

Comment 2: The methodological framework is vague and primarily descriptive. It could be better if system architecture presents data acquisition flow, virtual model synchronization, and AI diagnostic modules, supported by mathematical/algorithmic explanations

Authors’ answer: This study is a continuation of our previous work [36]. We have added various information throughout the text linking these studies. The previous work presented mathematical/algorithmic explanations and others information. (Lines 207-211, 215-218, 281-291)

 

Comment 3: The discussion conflates several adjacent concepts (digital twin, cyberphysical space, metaverse) without clarifying how the metaverse layer uniquely contributes beyond standard digital-twin technology. It could be better to connect the ideas together, at the moment, they feel isolated

Authors’ answer: Since the article discribe the low-code platform as a tool for rapidly deploying a metaverse, and the platform contains somewhat disparate functionality, this impression is created. The interaction between humans and industrial equipment on a different level and conception of metaverse described through scenarios.

Comment 4: The AI and diagnostic techniques are insufficiently detailed. If neural networks, vibration analysis, or time–frequency features are used, their structure, training data, and performance metrics must be described. Confusion matrix is there without other related matrix and comparative analysis

Authors’ answer: we tried to detail this information (Lines 281-291)

Comment 5: In the conclusion section, the authors should tone down the claims a little bit, which should correspond to the actual results and potential real-time usage. Moreover, explicitly outline the future steps for empirical validation

Authors’ answer: The conclusion section was changed (Lines 315-329)

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

1. The manuscript repeatedly refers to “diagnostic scenarios” and “functional blocks” without providing sufficient technical detail or architectural clarity. The descriptions are vague and lack formal definitions, making it difficult to assess the novelty or rigor of the proposed platform.

2. The explanation of the AI block and classification model is superficial. There is no mention of the model architecture, training parameters, validation strategy, or performance metrics (e.g., accuracy, precision, recall). Without these, the claim of successful training is unsubstantiated.

3. The manuscript suffers from numerous grammatical errors and awkward phrasing (e.g., “it allows to display,” “dataset a misalignment defect,” “basic principles training”). These issues severely hinder readability and professional presentation.

4. Terminology is inconsistently used. For example, “engine states,” “fault engine states,” and “defect” are used interchangeably without clear definitions. This undermines the scientific precision expected in a peer-reviewed publication.

5. The use of the NASA TLX questionnaire is mentioned, but no statistical analysis or interpretation of the results is provided:
   a. How was effectiveness measured?
   b. What were the scores?
   c. Were they statistically significant?

6. Some related works are recommended for citation:
   a. https://doi.org/10.1117/1.3556727
   b. https://doi.org/10.1109/JIOT.2024.3450813

Overall, The manuscript presents a potentially interesting application of low-code/no-code platforms in electric drive diagnostics, but it falls short in technical rigor, clarity, and experimental validation. A thorough rewrite is required, with attention to language quality, methodological transparency, and scientific substantiation.

Author Response

Dear reviewer,

We are sincere thankful to you for your valuable and helpful comments. We tried to have taken into account all comments and now we are submitting the revised version of our article. In addition to all comments, we would like to point out that human testing of the system does not comply with the ethical publication rules. Therefore, we are forced to remove the section on the NASA TSL test and correct the article in this part. Thank You.

With best regards.

Dr. Natalia Koteleva.

 

Comment 1: The manuscript repeatedly refers to “diagnostic scenarios” and “functional blocks” without providing sufficient technical detail or architectural clarity. The descriptions are vague and lack formal definitions, making it difficult to assess the novelty or rigor of the proposed platform.

Authors’ answer: We tried to correct it. For example, the description has been added for each functional block. (Table 1 Description) Additionally, a phrase emphasizing the novelty has been added. (Lines 310-313)

Comment 2: The explanation of the AI block and classification model is superficial. There is no mention of the model architecture, training parameters, validation strategy, or performance metrics (e.g., accuracy, precision, recall). Without these, the claim of successful training is unsubstantiated.

Authors’ answer: This information was added (Lines 281-291)

Comment 3: The manuscript suffers from numerous grammatical errors and awkward phrasing (e.g., “it allows to display,” “dataset a misalignment defect,” “basic principles training”). These issues severely hinder readability and professional presentation

Authors’ answer: We try to correct grammatical errors and awkward phrasing 

We changed phrases:

“it allows to display” to “which displays” (The line 17)

“dataset a misalignment defect,” to “a misalignment defect dataset” .(The line 22)

“basic principles training” to “the basic training approaches”. .(Lines 25-26)

 

Comment 4: Terminology is inconsistently used. For example, “engine states,” “fault engine states,” and “defect” are used interchangeably without clear definitions. This undermines the scientific precision expected in a peer-reviewed publication.

 

Authors’ answer: Fault engine states were deleted. Engine states refers to all possible engine states. We tried to correct abstract (Lines 17-28)

 

Comment 5: The use of the NASA TLX questionnaire is mentioned, but no statistical analysis or interpretation of the results is provided:

1. How was effectiveness measured?
2. What were the scores?
3. Were they statistically significant?

Authors’ answer: Unfortunately, we had to remove this part

Comment 6: Some related works are recommended for citation:

10. https://doi.org/10.1117/1.3556727
11. https://doi.org/10.1109/JIOT.2024.3450813

Authors’ answer: Unfortunately, the second link (b) is to a closed-access journal. We cannot cite this paper, although we would be happy to read and usage it. And the first link (a) probably contains an error, as it refers to a paper titled "Using admittance spectroscopy to quantify transport properties of P3HT thin films." In our opinion, it is not relevant to our work.

Comment 7: Overall, The manuscript presents a potentially interesting application of low-code/no-code platforms in electric drive diagnostics, but it falls short in technical rigor, clarity, and experimental validation. A thorough rewrite is required, with attention to language quality, methodological transparency, and scientific substantiation.

Authors’ answer: Thank you. We added some new information and tried to do our article better.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Thank you for the revision of the article and the explanations.

Reviewer 3 Report

Comments and Suggestions for Authors

All problems have been addressed.