Fault Feature Extraction Method of a Permanent Magnet Synchronous Motor Based on VAE-WGAN
Round 1
Reviewer 1 Report
I think it is suitable for publishing.Author Response
Dear reviewer:
We gratefully thanks for the precious time you spent making constructive remarks on our manuscript. Regarding your comment "English language and style are fine/minor spell check required", we have carefully considered and made some changes in the revised manuscript and highlighted them in yellow.
Best regards!
Yours sincerely,
Xiaowei Xu
Author Response File: 
Author Response.docx
Reviewer 2 Report
The paper is useful, please consider:
- its accuracy as 98.26%, it would be nice if the authors show the calculation.
- Figure 6 was not clear, increase the figure quality.
- The reason of using PMSM as fol-267 lows: rated power of 12 kW, rated speed of 1500 r/min.
- Figure 8-12,16 should be improved, the X-Y axis must be written.
- Line 347: what is " the original data in 4.4 s-4.48 347
s" - Line 374: what is "between 4.50 s - 4.57 s"
Author Response
Dear reviewer:
Thank you very much for your careful read and thoughtful comments on our manuscript. There is no doubt that these comments are valuable and very helpful for revising and improving our manuscript. We have carefully taken these comments into consideration in preparing our revision. In what follows, we would like to answer the questions you mentioned and give detailed account of the changes made to the original manuscript.
1.Comment：Its accuracy as 98.26%, it would be nice if the authors show the calculation.
Response to Comment：We have added the definition and calculation method of accuracy to the article. We add：
Accuracy is the percentage of correct results of the prediction in the total sample，that is, the ratio of the sum of the four correct classification in this article to the total sample.
2.Comment：Figure 6 was not clear, increase the figure quality.
Response to Comment：Thanks for your carefully check. We have changed the picture to add attachments, and it would be clearer.
3.Comment：The reason of using PMSM as fol-267 lows: rated power of 12 kW, rated speed of 1500 r/min.
Response to Comment：Thanks for reading carefully. Permanent magnet synchronous motor is one of the main driving motors used in new energy vehicles, and the 12-kilowatt power, 1500 rpm motor used in this article is one of the commonly used automotive motors. We use this type of motor as an example for experimentation.
4.Comment：Figure 8-12,16 should be improved, the X-Y axis must be written.
Response to Comment：Thank you for your significant reminding. The horizontal and vertical coordinates of these graphs represent the mean or variance of the hidden layer features. They are obtained by converting the data from high-dimensional to low-dimensional and mapping to two-dimensional space, so the horizontal and vertical coordinates of the graph have no unit. We have added horizontal and vertical coordinates to these figures, as shown in the following figure:
5.Comment：Line 347: what is "the original data in 4.4 s-4.48 s"
Response to Comment：We are very sorry for our problem with the English. We have changed "in 4.4 s-4.48 s" to "from 4.4 s to 4.48 s".
6.Comment：Line 374: what is "between 4.50 s - 4.57 s"
Response to Comment：Thank you again for your serious and responsible. We have corrected this problem and marked it. We have changed "between 4.50 s-4.57 s" to "from 4.50 s to 4.57 s".
We gratefully thanks for the precious time the reviewer spent making constructive remarks on our manuscript. If there are any other modifications we could make, we would like very much to modify them and we really appreciate your help. We hope that our manuscript could be considered for publication in your journal. Thank you very much for your help.
Best regards!
Yours sincerely,
Xiaowei Xu
Author Response File: Author Response.docx
This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.
Round 1
Reviewer 1 Report
see attached document
Comments for author File: Comments.docx
Author Response
Dear reviewers,
Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Fault Data Expansion Method of Permanent Magnet Synchronous Motor Based on Wasserstein-GAN”. Those comments are all valuable and very helpful for revisining and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. We feel sorry for the inconvenience brought to you, we carefully revised some English writing issues, added some parameters and references, and made certain changes to the diagram. Revised portion are marked in red in the paper. The main corrections in the paper and the responds to the reviewers’ comments are as flowing:
Responds to the reviewer’s comments:
- Comment:They can be improved by including more relevant literature in the field of electric motors, and it would be helpful if the introduction makes clear which faults are to be covered (we suggest to read this paper doi: 10.1177/1687814020944323).
Response to comment: Thank you for your recognition of our article(the paper doi: 10.1177/1687814020944323). We have included the faults covered in the revised manuscript.
- Comment: The sample data including the test setup and the motor, the hyperparameters, the model architectures, etc. are not sufficiently discussed in the paper.
11 and 188: From where were the real fault data of motor obtained (3000 sets of data, simulation/measurement at a test bench or an external source?)?
In which way were the 3000 sets of data divided into 2000 and 1000, in order to obtain first a mature and stable model?
Response to comment: Our data comes from the network, there are no specific hyperparameters, test settings, motors and other parameters, because this article aims at data expansion, not strict motor fault diagnosis. 3000 sets of data are randomly divided into 2000 and 1000. I am sorry for not using other data processing technology.
- Comment: 16-17: Do you mean here ‘testing’ instead of ‘training’? ‘Second, use the model to test on the remaining data to generate pseudo samples.’
Response to comment: We divide 3000 sets of data into two parts, 2000 sets of data as the training set, and the remaining data as the test set.So I think it is ”testing”.
- Comment:22: Are the results experimental (in real time) or have the trained models only been tested in simulation?
52-57: And it could not prove the good generalization ability of the trained models sufficiently.
Response to comment: This article proposes a data expansion method for the collected data, which only simulates the trained model, so it does not have good generalization.In the follow-up, we will conduct in-depth research and strive to achieve a good generalization ability.
- Comment:55-56: “the motor is susceptible… resulting in different levels of noise in the tested samples”: is this a result of tests? It seems very simplified.
Response to comment: “the motor is susceptible… resulting in different levels of noise in the tested samples”: it isn’t a result of the tests. It is a factor that may affect this experiment, and we must take this into consideration when we do the experiment. Some of these words may cause misunderstandings, and we have modified them.
- Comment: 73: The sentence here should be corrected. GAN model learns to generate new (fake) data with the same statistics as the training set (GAN is NOT a data classification method. But the discriminator part of that acts like a classifier).
Fig.1: The description for the feedback should be corrected. For example: tuning the model parameters (The discriminator role: to better distinguish between real and fake samples. The generator role: to fool the discriminator). The output of the discriminator is NOT the expanded samples. It is the classification results.
Response to comment: Thank you for your reminder and guidance . We modified it to “The GAN is an unsupervised probability distribution learning method”. And we have also modified the diagram according to your guidance.
- Comment:Section 2 (subsections 2.1 and 2.2) und subsection 3.1 are just the explanations of the well-known GAN and Wasserstein-GAN networks, like their main papers. The explanations and formulas were not referenced at all. Not all the parameters in the formulas are defined.
Response to comment: We gratefully appreciate for your valuable comment. But taking into account the opinions of the two reviewers and my own thinking, I think these formulas are necessary, and the following algorithm improvements involve related parameters.
- Comment:192-194: Why these inputs and outputs?
Response to comment: Turn-to-turn short-circuit faults are usually characterized by three characteristics: three-phase current, negative sequence current and electromagnetic torque. So we select them.
- Comment: The units of the figures 3 and 4?
Response to comment：Thank you so much for your careful check. But the values of D and G indicate the probability of the GAN generator and discriminator to generate and discriminate data, so there is no unit, and refer to relevant literature, there is no unit in this type of graph.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Dear Authors,
Here is a review of your manuscript ” Fault Data Expansion Method of Permanent Magnet Synchronous motor Based on Wassertein-GAN”
The idea of the manuscript is interesting; however, the manuscript is a bit messy and there are too few references, especially in the Introduction and on the motors. It seems like the authors are more comfortable in data analysis than on electric motors, and the text should be improved to show a background knowledge in rotating electrical machines. It feels like the authors have not read through and revised their work before submitting it, there are for example text in another language in several figures. The English should be improved. I suggest a careful and major revision.
More specific comments:
Add references to the introduction on Industry 4.0 and many more references on the PMSM and motors in EVs. This, to give the reader a good background to the work.
Please add references on the PM motor, and present, use and include the abbreviation (PMSM) in the manuscript.
“With its advantages, permanent magnet synchronous motors have be- 34 come one of the main motor types for new energy vehicles.” Describe what advantages that you are refereeing to.
Please consider changing the sentence at line 35: ” As the driving equipment, the 35 motor is the core of the new energy vehicle.”
If you are discussing the motor of an electric vehicle (EV), please refer to that.
In the introduction, there are a few statements which are not fully understandable by the reader and that should be explained better or removed, such as “Due to its complex working environment”. Is the motor in an EV in a more complex working environment than when it is usually used in other systems?
Consider changing word “automobile….”
40: data-driven, add word here: data-driven XXX …
“outstanding advantages” is a too positive expression, please tone it down, and add a reference to back your statements.
43: “t [2]. Such as m” The second sentence is “hanging a bit loose”, connect them better.
The introduction lacks references to work on the motor, please add more relevant references in this field.
Statements, such as: “. The current effective solution for coupling faults is to add sensors, and strive to realize the detection and diagnosis of coupling faults by adding monitoring means” needs reliable and recent references.
This should be removed: “which has important research significance.”, especially as no good references shows that you have looked into the recent research on fault monitoring of EV motors etc.:
Use other word than “they only…” and “ignoring” as it sounds too negative.
Use other words as it does not sound good in this context: “rich physical laws” and perhaps consider changing “connotative”.
Statements such as “many experts and scholars have proposed to apply it to the expansion” requires a few relevant references.
The abbreviation should be presented before it is used, and you use GAN before the formal introduction in “The Generative Adversarial Network (GAN) is well used”. So please adjust this.
Ensure that the use of the words generator and discriminator in the motor text makes sense to the reader. Generators and motors function in the same way, based on Maxwell’s laws, but I assume here that you refer to another type of generator, which is not fully clear for a reader with motor-background. Please clarify this if possible.
Something wrong in text ” reconstruction Make a comparison to” on line 87.
97: Not sure that it is correct to say “few literatures”.
Improve the English throughout the manuscripts, for example by the help of a native English speaker.
To me, it sounds like “Aiming at the problem of unbalanced fault data of rotating machinery, Li Qi et al. [16] 88 proposed an enhanced generative countermeasure network (EGAN) and established a 89 fault diagnosis model for rotating machinery” is an example of what you have done.
Change completely or remove line 104 to 108. The abbreviaitons etc. are not presented before they are used.
112: Not nice with they etc. and sound unprofessional in “They are generator G and discriminator D. They train and play games with each other”. Also, motors and generators are something else for rotating machine engineers, suggesting there may be a confusion here which should be acknowledged.
In the text, the notations should be neater and consistently in their looks. Now, it is a mixture between italics and other types of signs. There is a lack of references to the background of the Equations.
If there are any units of the parameters, please include this.
From 123 and onwards, the presentation of the parameters should be done in a better sentence.
Abbreviation is used before it is presented in “the Jensen-Shannon (JS) divergence”
Change “If the two distributions P and Q do not overlap regions, the KL divergence is meaningless.”
Change “The 1st-wassertein distance”. Ensure that it is consistent throughout the text with big or small W on Wasserstein.
Generally, as mentioned before, there is a lack of references to the background of the Equations used.
Perhaps move Figure 2 higher up, so that the sketch comes before the relevant equations, to support the reader in the process understanding.
I cannot comment on the accuracy of the equations.
“3.1. Subsection” change this title.
Change “T According to the”
172: Again, you have to be aware that a generator is something else when discussing motors.
176: The Equation should not be included like that in the sentence, and it should be numbered.
188: State clearly where the information and the data on the PMSM is taken from (add references). It is not clear where the authors have received info presented in line 192 to 197, please clarify this.
I am not sure that D and G has been explained in the text as abbreviations for discriminator etc. and perhaps use full words in Figure.
Change “When iterating 6000-8000 times in the figure, G reaches a stable state”
Consider moving parts of 3.3.1 to the Discussion.
Figure 5 includes signs which are not understandable for an English reader, change this.
The resolution of figures is too low and the text on the axis could be larger and clearer.
Change “n real data. And select representative data as markers. It”
“1000 sets of data are divided into 20 sets of samples” It has not been explained where the data sets come from.
“The experimental results are shown in Figures 247 5 and 6.” What is meant by experimental results here? If experiments with a motor have been conducted, this should be clearly stated and explained.
Be clear exactly which figure that is referred to in “torque in figure (a) is 4.3Nm” and “current in Figure (b) is 0.45A, an” and be consistent in writing Figure 6 (a) with big F. This occurs as well in next sections, so change this throughout the text.
I would split the results from the discussion, and add analysis such as “By comparing the other areas with lighter colors in Figure 5 and Figure 6, it can be found that the pseudo data generated by GAN basically conforms to the distribution characteristics of the original data.” to the discussion section instead as in the results.
Again, another language in Figure 6 and in Figure 7, Change this!
Change “el.In figure (a), th” etc.
Change sign, do not use ~ (tilde) when writing to, use a dash instead. “current of 0.38~0.6A and the B phase current of 269 0.975~0.985A.T”
There is a significant blend of results and discussions.
If there are two figures, (a) and (b), such as for Figure 7, the (a) and (b) should be under the correct figure, not together (a)(b).
Section 3.4.2 is partly a theoretical section, which I don’t think should be present in the result-section. Move the Theory to another section. Also, move all discussions to a separate Discussion section. Ensure that all figures have high enough resolution to look good for the readers.
The numbering of the Equations is wrong, and there is no Eq. 5.
“The picture 12 shows the original data of el” it is called Figure 12.
The overall impression of the paper is a bit sloppy, please fix this.
The references are mostly old, please include newer references.
Can the method proposed to be used at other motors as well?
Where did you receive data on the motor?
Why did you look into a PM motor? and describe characteristics, and compare with other motors without PMs.
Which companies would be interested in the proposed methodology?
Author Response
Dear reviewers,
Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Fault Data Expansion Method of Permanent Magnet Synchronous Motor Based on Wasserstein-GAN”. Those comments are all valuable and very helpful for revisining and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Thank you so much for your careful check, we carefully revised some English writing issues, added some parameters and references, and made certain changes to the diagram. Revised portion are marked in red in the paper. The main corrections in the paper and the responds to the reviewers’ comments are as flowing:
Responds to the reviewer’s comments:
Reviewer #2:
- Comment：“With its advantages, permanent magnet synchronous motors have be- 34 come one of the main motor types for new energy vehicles.” Describe what advantages that you are refereeing to.
Response to comment: We gratefully thanks for the precious time the reviewer spent making constructive remarks. We have increased the background of PMSM and the advantages of motors. That is, “Permanent magnet synchronous motor (PMSM), which uses permanent magnets to provide excitation, makes the motor structure simpler, saves the slip ring and brushes that are prone to problems, improves the reliability of motor operation, and because no excitation circuit is needed, no excitation loss, and the efficiency and power density of the motor are improved”.
- Comment: In the introduction, there are a few statements which are not fully understandable by the reader and that should be explained better or removed, such as “Due to its complex working environment”. Is the motor in an EV in a more complex working environment than when it is usually used in other systems?
Response to comment: We gratefully appreciate for your valuable suggestion. We explained the complex working environment of the motor that it has complex mechanical-electrical-magnetic-thermal coupling system, complex internal operating environment, small space, and poor heat dissipation conditions.
- Comment:112: Not nice with they etc. and sound unprofessional in “They are generator G and discriminator D. They train and play games with each other”.
Response to comment: Thank you so much for your careful check. We rewritten the sentences, but training and gaming are relatively professional words, and we did not find a better word to describe their relationship. “Conventional GAN consists of two parts, namely generator G and discriminator D, which train and play games with each other.”
- Comment: Section 3.4.2 is partly a theoretical section, which I don’t think should be present in the result-section. Move the Theory to another section.
Response to comment: Thanks for your suggestion, I adjusted this part to chapter two.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Although some of the points noted in the attached document were corrected in the publication, the more relevant issues, such as the origin of the data or questions about the evaluation of the data, were not further addressed or commented on. These points are marked in red in the attached document. Since these additions need a deep revision of the publication, I recommend a rejection of the publication at this point.
Comments for author File: Comments.pdf
Author Response
Dear reviewer,
Thank you for your letter and the reviewers' comments on our manuscript entitled "Wasserstein-GAN based method for extending fault data of permanent magnet synchronous motor". These comments are valuable and helpful for us to revisit and improve the paper, and they are important guidance for our research. We have carefully studied these comments and revised them, and we hope they will be accepted by you. We apologize for the inconvenience caused to you. We have carefully revised some English writing issues, added some references and analysis, and made some changes to the figures. The revised parts are marked in red in the paper. The main revisions in the paper and the responses to the reviewers' comments are listed below.
- Comment:They can be improved by including more relevant literature in the field of electric motors, and it would be helpful if the introduction makes clear which faults are to be covered (we suggest to read this paper doi: 10.1177/1687814020944323).
Response to comment: Thank you for your recognition of our article(the paper doi: 10.1177/1687814020944323). We have added the involved faults in line 48 of the revised version, reference 5.
- Comment:The sample data including the test setup and the motor, the hyperparameters, the model architectures, etc. are not sufficiently discussed in the paper.
Response to comment: Thanks to your reminder, we added the motor parameters in lines 196 and 197, the hyperparameters in line 236, and the model frame with related parameters from lines 221 to 228.
- Comment: The results of training and the validation analysis should be presented in a better and more consistent way.
Response to comment: Thank you for your comment, we are not thoughtful in the early consideration, the latter will be carefully improved in accordance with your comments.
- Comment: Possibly, an outlook on possible continuations of the investigations should be given.
Response to comment: Thank you for your suggestions,We have added an outlook on W-GAN at the end of the paper.
- Comment: 11 and 188: From where were the real fault data of motor obtained (3000 sets of data, simulation/measurement at a test bench or an external source?)? The performed data acquisition or the source of the data used should be clarified. Also, the type of the motor used and its operating points during the data acquisition should be
discussed/showed more in details.
There is no information about hyperparameters and model architecture selection in the paper
Response to comment: Thank you for your comments. The data we used is from an online motor with a rated power of 12 kW, a rated speed of 1500 r/min, a motor with 10 poles, 45 stator slots and water-cooled cooling. In this paper, we mainly discuss the data expansion method and do not consider much about the operation points in the data collection process, which is the lack of our consideration and we will improve it later.
- Comment: In which way were the 3000 sets of data divided into 2000 and 1000, in order to obtain first a mature and stable model? E.g., Randomly? Why weren't other techniques of data preprocessing (Feature Engineering) used, which are very common for machine learning algorithms?
Response to comment: Your comments are of great importance to this article. We randomly divided 3000 sets of data, and divided the test samples more because we considered the subsequent need for samples to be input to the constructed generator and discriminator, which are used to update the loss function. Later we will carefully consider how to use other methods for data pre-processing.
- Comment: 16-17: Do you mean here ‘testing’ instead of ‘training’? ‘Second, use the model to test on the remaining data to generate pseudo samples.’
Response to comment: Thanks for checking, we have fixed it to "train".
- Comment: 22: Are the results experimental (in real time) or have the trained models only been tested in simulation?
52-57: And it could not prove the good generalization ability of the trained models sufficiently.
Response to comment: This article proposes a data expansion method for the collected data, which only simulates the trained model, so it does not have good generalization.In the follow-up, we will conduct in-depth research and strive to achieve a good generalization ability.
- Comment: 25: As it is mentioned in the paper, that data analysis results show that the improved Wasserstein-GAN effectively solves the problem of poor convergence of GAN. But it isn’t actually presented in the paper.
Section 3.3.1: How about the Wasserstein-GAN stability? The stability criteria should be described more clearly and more in detail.
Section 3.4.1:The comparison between the diagrams is only visual. In the b) diagrams it is not clear whether GAN or WGAN is better.
365: The convergence effect or the stability analysis of the Wasserstein-GAN was not shown and compared with that of GAN. But in the conclusion is mentioned that it was better.
The results of training (for both GAN and Wasserstein-GAN) are not discussed enough.
Response to comment: Your suggestion is a great guidance for our paper, and we have added the plots of Wasserstein distance for GAN and W-GAN in Section 4.3, from which we can see that the improved method in this paper has better convergence and stability.
- Comment: 52-57: As it is mentioned in the paper, the motor is susceptible to electromagnetic, temperature and other environmental factors, resulting in different levels of noise. How can it be ensured that the trained models (e.g., the Wasserstein-GAN) will work well in other situations (other operating points, temperatures, etc.) too and does not overfit to the training data? E.g., performing some regularization techniques could improve the generalization ability of the trained models. In addition, the test samples used to validate the trained models are very similar to the training samples, what could not prove the good generalization ability of the trained models sufficiently.
55-56: “the motor is susceptible… resulting in different levels of noise in the tested samples”: is this a result of tests? It seems very simplified.
Response to comment: Your comments are significant for the future direction of our research. “the motor is susceptible… resulting in different levels of noise in the tested samples”: it isn’t a result of the tests. It is a factor that may affect this experiment. At present we cannot guarantee that the trained model will work well in other situations as well, and we cannot control well for changes in the environment, which does have some impact on our data expansion model, and we will follow up with more research in this area.
- Comment: 124: There, the variables should be without tilde.
Response to comment: In the article we used to Indicates the generated data and referenced the literature doi: 10.1109/ICMA.2019.8816198, for your comment we changed this to x’.
- Comment: 146: The name of the discriminator network changes to critic network in a Wasserstein-GAN.
Response to comment: Thank you for checking, we have revised it.
- Comment:The explanations and formulas were not referenced at all.
Reference all the equation
There is a paper in this field with doi: 10.1109/ICMA.2019.8816198, which addresses the same topic in a similar way (like figure 1 and formula 2). Please reference all the used information.
Response to comment:Thank you for your comments. But I think these formulas are necessary for improving W-GAN in this paper, and we have added references31-34 to the formulas that are necessary, and we have not added references for this due to the generality of the formula for calculating the correlation coefficient.
- Comment:Fig. 2: Random initialization of the model leads to unfair training results, when comparing the two GAN and Wasserstein models together. To initialize the parameters of both models equally at the beginning of the training, a pseudo-random number generator could be used, for example.
Response to comment: Thank you for your comments. We refer to the literature on "A sample enhancement method for power system transient stability assessment based on improved CGAN" and initialize the parameters of both models equally using normalization.
- Comment:183: loss, twice
Response to comment: Thank you for checking, we have deleted one.
- Comment:The units of the figures 3 and 4?
Response to comment: Thank you so much for your careful check. But the values of D and G indicate the probability of the GAN generator and discriminator to generate and discriminate data, so there is no unit, and refer to relevant literature, there is no unit in this type of graph.
- Comment:Section 3.4.1: What is the reason for the chosen parameter-combination? Why do darker areas show the fault situations more concentrated? What are the representative data?
Response to comment: Thanks to your comments, we have placed the reasons for choosing these parameters for this paper in lines 198 to 202 of the revision. We put the electromagnetic torque and the values of phase A current, negative sequence current and phase B current in the same time domain in the same coordinate system to form a probability cloud, the more points the darker the color of the area, the darker the color can represent whether the fault data is concentrated at this time.
- Comment:224-225: Why eigenvalues in this context?
Response to comment: Thank you for your comments. We call the values of phase A and B currents, negative sequence currents and electromagnetic torque in the same time domain placed in the same coordinate system as eigenvalues, not eigenvalues. It is possible that our naming is wrong and we will investigate further.
- Comment:Section 3.4.2: The figures show only a small partition of the data. What does the course of an electrical period, e.g. of the current signal, look like?
Response to comment: Thank you for your valuable comments, we are not well thought out in the early stage, did not draw the complete electric cycle process, later will be seriously improved.
- Comment:Here, non-linear coefficients should also be considered in order to better distinguish the methods from each other. For example by using MSE or RMSE functions, which are better for regression problems.
Response to comment: Thank you for your valuable comments, we did not consider well in the early stage, we will seriously improve later.
- Comment:347-352: make no sense. Revise these sentences.
Response to comment: Thank you for your comments. In our first version of the manuscript, lines 347 through 352 show Figures 14 and 15, showing a comparison of the original negative sequence current and the expansion data and the CORREL function values for the expansion data, which I think are useful to illustrate the effectiveness of the negative sequence current expansion method.
- Comment:The conclusion and the main results of the paper should be discussed in more details.
Response to comment: Thank you for your valuable comments. We have added some details at the conclusion.
Best regards!
Yours sincerely,
Xiaowei Xu
Author Response File: Author Response.pdf
Reviewer 2 Report
I very much appreciate that the paper has been improved according to many of the comments.
I don't think that the first part of the paper, in the introduction, is well written and appropriate in a journal paper.
In lines 55-59 there are a mixture between big first letters on Methods and small on methods.
Author Response
Dear reviewer,
Thank you for your comment on our manuscript entitled "Wasserstein-GAN-based Permanent Magnet Synchronous Motor Fault Data Extension Method". These opinions are very valuable and very helpful to the revision and improvement of our paper. We carefully studied the comments and made corrections, hoping to get approval. We carefully revised the capitalization of the first letter of words and re-written the first paragraph of the introduction. I have also made some changes to the details of the article, and the revised parts are marked in red in the paper. Finally, thank you again for your valuable comments on this article.
Best regards!
Yours sincerely,
Xiaowei Xu
Author Response File: Author Response.pdf
