PONE-D-25-33127Real-Time Motor Operating State Recognition via Multi-Sensor Fusion: A Wavelet–Neural–Evidence Framework for Industrial Condition MonitoringPLOS ONE

Dear Dr. Peng,

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Kannadhasan Suriyan

Academic Editor

PLOS ONE

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AUTHOR SHOULD SUBMIT THE REVISED PAPER AS PER REVIEWER COMMENTS

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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?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

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 requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper presents a multi-sensor fusion framework for intelligent motor condition monitoring, which integrates wavelet-based feature extraction, shallow neural network classification, and evidence-theoretic decision fusion. The work is relatively thorough, but before the publication of the paper, further revisions are needed based on the feedback.

1.The innovation of this paper is not prominent. Wavelet packet transformation, BP neural network, and DS fusion are all mature methods. Although their combination has practical value, it lacks theoretical innovation and algorithm improvement.

2.In the first paragraph of the introduction, it is stated that motors are prone to faults such as bearing wear, rotor imbalance, insulation breakdown, and thermal stress. However, in the experimental design, only three states - "shutting down, no-load, and load" - were tested, without including the fault states. Please provide a reasonable explanation for this.

3.Please explain the reasons for the selection of wavelet packet decomposition parameters (as stated in "Signal Processing and Feature Extraction Method": the db4 wavelet was chosen for wavelet packet decomposition and a 3-level decomposition was performed). Also, conduct a sensitivity analysis of different parameters on the results.

4.In "State Recognition Model Construction", it is mentioned that "the model parameters are optimized using the Adam optimizer, with an initial learning rate of 10^-3, and following a decay schedule. An early stopping strategy based on validation loss is adopted to prevent overfitting and improve generalization ability". It is recommended to supplement the specific strategy for learning rate decay and the specific criteria for early stopping.

5.The title clearly includes "Real-Time Motor Operating State Recognition", while the abstract emphasizes "real-time monitoring" and "low-latency data acquisition". However, the full text does not demonstrate real-time performance. Please add this information.

6.There is a lack of detailed explanation regarding the selection of hyperparameters for the three-layer feedforward backpropagation neural network. Please provide the necessary information.

Reviewer #2: 1. Faulty conditions are not considered in the experiments.

2. Data samples are not sufficient. Also single motor data only is collected. Consider various motors for data collection.

3. Needs to perform various statistical analysis

4. Needs to do comparative analysi

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Reviewer #1: No

Reviewer #2: No

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PONE-D-25-33127R1Real-Time Motor Operating State Recognition via Multi-Sensor Fusion: A Wavelet–Neural–Evidence Framework for Industrial Condition MonitoringPLOS ONE

Dear Dr. Peng,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

ACADEMIC EDITOR: Minor revision==============================

Please submit your revised manuscript by Nov 03 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at 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.

Please include the following items when submitting your revised manuscript:

• 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,

Agbotiname Lucky Imoize

Academic Editor

PLOS ONE

Journal Requirements:

1. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. 

2. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Reviewer #3: Revise the paper accordingly, but no not include the self-published papers suggested by the reviewer, especially the ones not related to the current study.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #3: Yes

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

Reviewer #1: Yes

Reviewer #3: Yes

**********

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #3: Yes

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #3: This revised manuscript proposes a real-time condition-classification pipeline that extracts multiscale features with wavelet packet decomposition, feeds per-channel classifiers, and fuses decisions at the output. The authors also report embedded latency to support deployability. The revision is substantially improved and addresses most prior concerns. Before acceptance, I suggest the following focused clarifications and small additions.

- Clarify and guard against data leakage

You state that continuous recordings were windowed into 1024-sample segments with 50% overlap and then split 60/20/20. Please make explicit that the split is done at the recording/session (or subject) level—not at the window level—so that no windows from the same 10-minute session appear in both training and test sets.

- Provide a code/firmware repository link

You note dataset availability, but there is no link to the processing/training code and the embedded inference code. Public code greatly improves reproducibility and speeds up adoption.

Share a minimal GitHub repository (preprocessing, feature extraction, training, inference; plus a simple script to reproduce one main table/figure).

- Discuss complex and overcomplete wavelets as a natural extension

Your results motivate a short Discussion paragraph on why complex/overcomplete wavelet families (e.g., dual-tree complex, rational-dilation, or tunable Q-factor designs) may further improve shift-robustness, directional selectivity, and transient capture, potentially benefiting early-fault signatures.

Cite the following works:

https://doi.org/10.1109/ACCESS.2024.3524763

https://doi.org/10.1109/IEMBS.2011.6091193

https://doi.org/10.1109/EMBC.2014.6943877

https://doi.org/10.1007/s11517-014-1224-0

https://doi.org/10.1016/j.bspc.2010.09.007

https://doi.org/10.1016/j.compbiomed.2018.11.004

https://doi.org/10.1007/s11517-013-1114-x

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7. PLOS authors have the option to publish the peer review history of their article (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 #1: No

Reviewer #3: No

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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.

Real-Time Motor Operating State Recognition via Multi-Sensor Fusion: A Wavelet–Neural–Evidence Framework for Industrial Condition Monitoring

PONE-D-25-33127R2

Dear Dr. Peng,

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,

Agbotiname Lucky Imoize

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Accept in current form.

Reviewers' comments: