CARE to Compare: A Real-World Benchmark Dataset for Early Fault Detection in Wind Turbine Data
Round 1
Reviewer 1 Report
Comments and Suggestions for Authors
Public datasets presented in the manuscript is very meaning for predictive maintenance of wind turbines. The work fit the journal scope. The paper is well prepared and interpreted appropriately. However, there are several problems that need to be addressed:
1. The data source “Fordatis”: http://dx.doi.org/10.24406/fordatis/343 is not available online.
2. A very similar paper was submitted to Elsevier. Just make sure the paper is not duplicate publication.
3. The paper proposes a new scoring method, called CARE. But there is no classic scoring method that can be compared to it to demonstrate the superiority of the newly proposed scoring method. And how to evaluate the effectiveness of the four sub-scores?
4. The tables in the manuscript don’t have corresponding headers.
5. Although the time stamps are anonymized, it is unreasonable to label future date timestamp in the data, e.g. 2026, 2027, 2028 in Wind Farm B datasets.
Author Response
Comment 0: Public datasets presented in the manuscript is very meaning for predictive maintenance of wind turbines. The work fit the journal scope. The paper is well prepared and interpreted appropriately. However, there are several problems that need to be addressed:
Response 0: We thank the reviewer for the positive feedback and their helpful comments below.
Comment 1: The data source “Fordatis”: http://dx.doi.org/10.24406/fordatis/343 is not available online.
Response 1: We have removed the link to the Fordatis. The dataset is available on Zenodo.
Comment 2: A very similar paper was submitted to Elsevier. Just make sure the paper is not duplicate publication.
Response 2: The paper that we submitted to Elsevier was retracted; this Journal fits the topic better.
Comment 3: The paper proposes a new scoring method, called CARE. But there is no classic scoring method that can be compared to it to demonstrate the superiority of the newly proposed scoring method. And how to evaluate the effectiveness of the four sub-scores?
Response 3: We added subsection 3.3 which elaborates on the advantages of the CARE-score in comparison to other scoring methods.
Comment 4: The tables in the manuscript don’t have corresponding headers.
Response 4: We added captions and number to the tables.
Comment 5: Although the time stamps are anonymized, it is unreasonable to label future date timestamp in the data, e.g. 2026, 2027, 2028 in Wind Farm B datasets.
Response 5: We changed the anonymization procedure for the time stamps and described it in section 3.1.4. We also released a new Version of the dataset on Zenodo (https://doi.org/10.5281/zenodo.14006163), where the time stamps have been edited accordingly. No future dates are present within the dataset anymore.
Remark:
The attached PDF-File highlights all differences between the reviewed manuscript and the revision. Removed text is marked in red while new additions are highlighted in blue.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for Authors
The paper is very well written and presented, with a solid structure and clear background and aims.
I particularly appreciate the publication of a large dataset on wind turbines.
Author Response
Comment 1:
The paper is very well written and presented, with a solid structure and clear background and aims.
I particularly appreciate the publication of a large dataset on wind turbines.
Response 1:
We thank the reviewer for the positive review.
Remark:
The attached PDF-File highlights all differences between the reviewed manuscript and the revision. Removed text is marked in red while new additions are highlighted in blue.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for Authors
The authors provide a new dataset for wind turbines early fault detection to reduce the limitations of rare public data for WT benchmarks. Meanwhile, the authors propose a new scoring method, called CARE to identify good early fault detection models for wind turbines. Overall, this is a very meaningful work.
The topic is original and relevant to the field. The paper is well prepared. The conclusions consistent with the evidence and presented arguments. The manuscript is recommended to be accepted after minor revisions.
One minor suggestion, add corresponding references to the score methods and equations in section 3.2.1 and 3.2.2.
Author Response
A PDF-file highlighting the differences between revision 2 and revision 1 can be found in the attachment.
Comment 1:
The authors provide a new dataset for wind turbines early fault detection to reduce the limitations of rare public data for WT benchmarks. Meanwhile, the authors propose a new scoring method, called CARE to identify good early fault detection models for wind turbines. Overall, this is a very meaningful work.
The topic is original and relevant to the field. The paper is well prepared. The conclusions consistent with the evidence and presented arguments. The manuscript is recommended to be accepted after minor revisions
Response 1:
We thank the reviewer for the positive feedback and their helpful comments below.
Comment 2:
One minor suggestion, add corresponding references to the score methods and equations in section 3.2.1 and 3.2.2.
Response 2:
We added a reference to the scoring criteria in section 3.2.1, as well as references for the accuracy-score and the F-beta-score in section 3.2.2.
Author Response File: Author Response.pdf
