Dear Dr. Dave,

plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

John Adebisi, Ph.D

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: No

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: No

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: The article presents a machine learning (ML) and deep learning (DL) framework for predictive maintenance of centrifugal pump machines (CPM), focusing on automating condition monitoring and fault detection. The authors highlight the limitations of traditional monitoring methods, such as manual data analysis, which is time-consuming and lacks adaptability across various operating conditions. By implementing a five-point summary of the data, exploratory data analysis (EDA), feature engineering, and data visualization, the study seeks to enhance data quality before model training. The ML models used, including random forest and deep learning classifiers, are evaluated based on metrics such as accuracy and F1-score, achieving high precision in detecting pump conditions. The proposed approach demonstrates an efficient, robust alternative for condition monitoring in industrial applications, with the potential for broad adaptation in predictive maintenance workflows.

Attached are my recommendations for the article.

Reviewer #2: (1) The introduction discusses general challenges in centrifugal pump optimization but can be improved by explicitly stating the unique contributions of this study. For instance, clearly highlight how the Dewsoft FFT DAQ system and the applied machine learning techniques improve upon existing methods in terms of predictive accuracy and operational efficiency.

(2) The section on hypothesis testing (page 14, Figure 5) is well-structured but could benefit from a brief comparison of the results obtained through Z-tests and ANOVA for different preprocessing scenarios. Explain how these tests validate the absence of bias and their implications for the robustness of the machine learning models.

(3) Some figures, such as the heatmap on page 20 (Figure 12), are informative but lack sufficient descriptions to guide readers unfamiliar with correlation matrices. Add a legend or a few explanatory sentences to clarify key observations, such as which correlations are critical for predictive maintenance.

(4) While the paper presents model performance (Tables 1–4, pages 24–29), the discussion should include a comparison of computational efficiency or training times across models like Naïve Bayes, SVC, and Random Forest. This would provide practical insights into model selection for real-time deployment.

(5) The manuscript effectively discusses data preprocessing and modeling but should connect these findings to practical scenarios. For example, explain how the insights from clustering (e.g., Figures 13 and 14 on page 22) could inform maintenance schedules or reduce downtime in industrial settings.

(6) I think the paper could benefit from the follwoing papers that greatly inestigated the optimal ML workflow:

https://doi.org/10.15530/urtec-2024-4044244

https://doi.org/10.1016/j.geothermics.2024.103028

https://doi.org/10.2118/219231-MS

https://doi.org/10.3390/en15238835

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what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #1: No

Reviewer #2: Yes:  Taha Yehia

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[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step.

Attachments

Attachment

Submitted filename: Review.pdf

Dear Dr. Dave,

plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

John Adebisi, Ph.D

Academic Editor

PLOS ONE

Comments from PLOS Editorial Office : We note that one or more reviewers has recommended that you cite specific previously published works in the current and previous rounds of revision. As always, we recommend that you please review and evaluate the requested works to determine whether they are relevant and should be cited. It is not a requirement to cite these works and you may remove any added citations before the manuscript proceeds to publication. We appreciate your attention to this request.

Additional Editor Comments:

Dear Authors

Your manuscript has been given a major revision due to some observations by one of the reviewers especially who have requested a Major revision to improve clarity, methodological rigor, and language. The authors should address inconsistencies in statistical interpretations, ensure ML model transparency, and refine figures/tables for readability.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: (No Response)

Reviewer #3: All comments have been addressed

Reviewer #4: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #2: (No Response)

Reviewer #3: Yes

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #2: (No Response)

Reviewer #3: Yes

Reviewer #4: No

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Reviewer #2: (No Response)

Reviewer #3: Authors have addressed all the comments successfully. Good luck for future work in this research area

Reviewer #4: Major revision required to improve clarity, methodological rigor, and language. The authors should address inconsistencies in statistical interpretations, ensure ML model transparency, and refine figures/tables for readability.

• The abstract presents a broad summary but does not include numerical results. Sentences like "this approach improves operational efficiency" need quantification (e.g., percentage increase in efficiency, reduction in downtime).

• The phrase "the quality of data recorded plays the important role" is grammatically incorrect and should be revised for readability. Additionally, "machine learning (ML) & Deep Learning models (DL)" should be consistently formatted throughout the paper.

• Weak Problem Statement. The introduction mentions challenges in centrifugal pump monitoring but does not specify why current machine learning techniques are insufficient. The research problem should clearly define the existing gaps in literature with specific citations.

• Lack of Theoretical Background. The study mentions the application of ML and AI but does not provide a theoretical framework linking these methods to predictive maintenance or anomaly detection in pump systems. Referencing established theories in condition monitoring and fault detection would strengthen the argument.

• The same references appear multiple times without adding new insights. The literature review should be more structured, categorizing studies into specific themes (e.g., feature engineering, predictive maintenance, deep learning architectures).

• The paper mentions feature engineering techniques but does not explain why specific feature selection methods (e.g., PCA, mutual information) were chosen over others. A justification for the methodology is needed.

• Some papers are important to cite in the literature.

1. Active learning-based machine learning approach for enhancing environmental sustainability in green building energy consumption

2. Integrating machine and deep learning technologies in green buildings for enhanced energy efficiency and environmental sustainability

• Inadequate justification of data selection and sample size.

• The dataset consists of 70,062 rows and 13 columns, but the paper does not explain why this data size is appropriate for training ML models. Was a power analysis or cross-validation strategy applied to confirm adequacy?

• The study mentions data cleaning and transformation, but does not describe how missing values, outliers, or imbalanced classes were handled. These preprocessing steps are crucial for ML performance and should be explicitly detailed.

• While the paper presents means and standard deviations, there is no explanation of what these values imply in the context of pump optimization. Does higher variance in sensor data indicate potential faults?

• The regression analysis claims that sensor temperature has a strong correlation with performance, but this contradicts previous claims that flow rate is the primary predictor

• The study reports high ML accuracy (99.99%), but does not compare against simpler baseline models (e.g., logistic regression). Without a benchmark comparison, the claims about model superiority are unconvincing.

• The deep learning model achieved 100% accuracy on both training and testing data, which suggests severe overfitting. Did the authors apply regularization techniques (dropout, L2 penalty) to mitigate overfitting?

• The conclusion overstates the success of ML models without acknowledging limitations, such as potential biases in sensor data, generalizability to different pump types, and interpretability challenges.

• Significant grammatical errors and formatting inconsistencies.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #2: Yes:  Taha Yehia

Reviewer #3: No

Reviewer #4: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step.

Innovative Data Techniques for Centrifugal Pump Optimization with Machine Learning & AI Model.

PONE-D-24-46901R2

Dear Dr. Dave,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

John Adebisi, Ph.D

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

Reviewer #2: (No Response)

Reviewer #3: Authors have addressed all the comments successfully. I wish him good luck for future work in area of AI.

Reviewer #4: After carefully reviewing the authors' responses and revisions, I find all concerns have been addressed thoroughly. The paper now meets the journal's standards, and I recommend acceptance in its current form.

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what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

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