Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

 

The authors propose a new method to detect broken bars in induction motors, however this reviewer has some comments.

 

The article seems a bit messy, there is too much information, theoretical framework not directly related to the work, the state of the art is reviewed twice, please simplify and tidy your article.

 

Four times the acronym FDD is defined, three times IM.

Section 2.2 repeats a state of the art

 

Please insert the theoretical background first before the methodology instead of section 2.2

Answer what is a shadow

 

In Section 3.1, line 455, what is EEC?

 

Section 3.1 is called “Digital shadow for real-time visualization of losses and efficiency through motor parameters” but it never talks about it.

 

Please limit your theoretical background only to what is necessary for your work.

 

Line 480, what is the methodology of this survey?

Line 482 Is there a reference 0??

Line 512 what is 00?

Can you give a diagram of your methodology in section 3?

Is the fault detection network a neural network?

Author Response

Reviewer #1:

Comment: The authors propose a new method to detect broken bars in induction motors, however this reviewer has some comments.

 

1. The article seems a bit messy, there is too much information, theoretical framework not directly related to the work, the state of the art is reviewed twice, please simplify and tidy your article.

Response: Thank you for your comments. We went through the manuscript and made amendments as per your concerns. In particular, we revised the section 2 to remove redundancy and theoretical content that was not directly related to this work. The whole article has been shortened by removing excess information which makes it more precise and focused. We think these adjustments have improved the manuscript’s coherence and lucidity; kindly go through it again in its updated version.

 

2. Four times the acronym FDD is defined, three times IM.

Response: Thank you for comment, this was corrected.

 

3. Section 2.2 repeats a state of the art
4. Please insert the theoretical background first before the methodology instead of section 2.2

Response: Thank you for your comments. We have rearranged Section 2 to function as “Theoretical Background” in the revised version of the paper, moving it ahead of the methodology as you suggested. Furthermore, we have introduced a new section titled “Novelty and Significance of Our Work” in place of Section 2.2 where we give a brief summary of related works and clearly point out our contributions. In this way, we ensure that this part agrees with the rest of the document’s progression, while also avoiding repetition of the state-of-the-art literature.

 

5. Answer what is a shadow

Response: Thank you for your question. In our paper, the term "shadow" refers to a digital system that replicates the real-time efficiency of a physical induction motor. In industrial settings, directly monitoring motor efficiency with simple data acquisition systems can be challenging because certain parameters cannot be measured physically. To overcome this, a model is used to estimate these unmeasurable magnitudes. This model-based system for monitoring data is what we call a "digital shadow."

The digital shadow continuously tracks motor losses and efficiency through a model, providing critical insights into the motor's performance. However, it is important to note that the digital shadow is purely observational. It does not influence or control the physical motor because there is no feedback loop connecting the digital model to the physical system. Essentially, it functions as a one-way real-time data acquisition system.

Since the digital shadow model is not the primary focus of this paper, we have provided only a brief summary. For a more detailed explanation of the efficiency-based shadow model for induction motors, please refer to our previously published paper [1].

 

6. In Section 3.1, line 455, what is EEC?

Response: EEEC= Electrical Equivalent Circuit. Based on your comments, we make sure in the revised manuscript, all the abbreviation are defined before using them.

 

 

7. Section 3.1 is called “Digital shadow for real-time visualization of losses and efficiency through motor parameters” but it never talks about it.

Response: Thank you for your comments. Yes, Section 3.1 may not have clearly addressed the role of the Digital Shadow as intended. To avoid redundancy, we initially chose not to delve into the Digital Shadow, as we have already discussed it extensively in two previous publications. These earlier studies form the foundation for our current work, enabling us to develop a fault diagnosis method through loss prediction.

In this paper, the Digital Shadow system is used to provide real-time visualization of motor losses and efficiency. By comparing a healthy motor with a faulty one, we can visualize and analyze differences in losses and efficiency, which is crucial for the development of the proposed fault diagnosis method. We will revise Section 3.1 to more explicitly connect these concepts and clarify the importance of the Digital Shadow in this context.

 

8. Please limit your theoretical background only to what is necessary for your work.

Response: Thank you for your feedback. Your comment is considered in the revised manuscript.

 

9. Line 480, what is the methodology of this survey?

Response: Thank you for your question. The methodology for this survey is as follows:

Step N°	Questions	Tools/Method	Actions	Output
1	What factors contribute to the reduction in motor efficiency?	Search engines: Google scholar, Google, Chat GPT.	Search using the keywords: •       Google Scholar: "Induction Motor Loss Sources“, "Induction Motor Efficiency Reduction Factors“, "Causes of Induction Motor Inefficiency“, "Faults in Induction Motors and Efficiency Impact" •       Google: "Induction motor inefficiency causes“, "Factors reducing induction motor efficiency“, "Induction motor losses and solutions“, "Impact of faults on induction motor performance" •       ChatGPT: "Factors affecting induction motor efficiency“, "Common sources of induction motor losses“, "How faults impact induction motor efficiency“, "Induction motor performance issues and solutions“ Results Analysis: Analysis of research papers, search engine results, and expert knowledge.	List of 67 factors that reduce motor efficiency. These factors represent consequences of faults in induction motors or improper operation. We refer to them as "common sources."
2	Are these common sources exhaustive, or are they interrelated?	Method: Cluster Analysis	Comparative analysis: Examine the common sources to identify similarities, group them into a single category, and then treat this category as the primary common source.	List of 33 uncorrelated common sources.

 

 

10. Line 482 Is there a reference 0??

Response: The reference should have pointed to Table 1, but due to some error the link to the table was not correctly made. This has been done in the revised manuscript.

 

11. Line 512 what is 00?

Response: The “00” was a reference number that was missing, and should have pointed to Table 3. This error has also been corrected in the revised manuscript.

 

Moreover, all the missing references in the manuscript have been checked and revised wherever necessary for accuracy and uniformity.

 

12. Can you give a diagram of your methodology in section 3?

Response: Thank you for your suggestion. In response to your comment, we have included a diagram in section 3 that illustrates the proposed methodology. In addition of figure 2,3, and 5 this diagram has been designed with the view to provide more comprehensible representation of our steps and processes.

 

13. Is the fault detection network a neural network? 

Response: I would like to thank you for your question. The fault detection network is not a neural network. It is a custom network we developed based on our analysis of the inefficiency sources connected to motor faults which in turn are associated with losses. The network is developed as follows: When a certain loss exceeds its rated value, the parameters concerned to this loss are scrutinized. We do so by looking at some possible causes of these parameter changes by identifying common sources that could lead to such variations. These common sources are then connected with potential fault and as such, diagnosis of the fault is done by identifying how the common sources are connected to observed losses.

Hence, it is recognized that farther experiments are possible and can investigate the inclusion of neural networks to improve this fault detection network. Specifically, a two-stage neural network approach could be considered: the first one could translate single fault parameters to the common sources and the second one could perform the same to translate the common sources to motor parameters or losses.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This is a meaningful paper, but there are some issues that need to be noted below.

1. The author should emphasize the innovation of this article

2. This article extensively introduces the algorithm mechanism, but some formulaic theoretical explanations should be provided.

3、Figure 8 should provide a premise for comparing motors under different faults. Is the comparison conducted at the same voltage?

4、The unit in Figure 9 is missing.

Author Response

Reviewer #2:

Comment: This is a meaningful paper, but there are some issues that need to be noted below.

 

1. The author should emphasize the innovation of this article

Response: Thank you for your comment. We recognize the importance of highlighting the innovative aspects of our work. In response, we have revised the manuscript to better emphasize the key innovations presented in this article. Specifically, we have made the following updates:

• Highlighting Novel Contributions: We have clearly outlined the unique contributions and advancements introduced in this study in the "Novelty and Significance of Our Work" section. Our approach to fault diagnosis, which focuses on an efficiency-based shadow approach, is unprecedented in the literature. This paper represents the first application of the digital shadow system we have developed.
• Detailing Innovation Aspects: We have emphasized that the fault diagnosis network is the primary innovation of this paper, showcasing how it differentiates our work from existing research.
• Showcasing Practical Impact: The method we have developed is specifically designed for Industry 4.0 applications, highlighting its practical relevance and potential impact.

These revisions ensure that the innovative elements of our research are prominently featured and clearly communicated to readers. Please refer to the contribution paragraph and the "Novelty and Significance of Our Work" section for more details.

 

2. This article extensively introduces the algorithm mechanism, but some formulaic theoretical explanations should be provided.

Response: Thank you for your feedback. We understand that the article primarily focuses on the algorithm mechanism, as it builds on concepts introduced in a previously published paper [1], where the digital shadow system is thoroughly described. To avoid redundancy, we provided only a summary of the digital shadow system in this paper, including the data acquisition process. We recognize that readers who are unfamiliar with the previous work may find it challenging to understand the predictions of losses and efficiency without additional context. Therefore, we will include the relevant equations and theoretical explanations in the revised manuscript for clarity.

 

3、Figure 8 should provide a premise for comparing motors under different faults. Is the comparison conducted at the same voltage?

Response: Yes, the comparison is conducted under the same control conditions. However, it is important to note that the severity of the fault can affect the characteristics of both the voltage and current, which in turn may influence the observed loss characteristics.

 

4、The unit in Figure 9 is missing.

Response: Thank you for your comment, the figure is modified accordingly.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

  The authors responded extensively and correctly to my comments. No further comments from me.