A Time-Series Data Generation Method to Predict Remaining Useful Life
Round 1
Reviewer 1 Report
- More information and figures on experimental setup and the batteries are needed.
- Figures need grids, and tables need middle borders.
- All abbreviations need to be explained.
- The probability and datasets need more visual presentations in the form of graphs.
- References need to be reformatted based on the template.
- Please make sure the indentation format is consistent.
Author Response
"Please see the attachment."
Author Response File: 
Author Response.docx
Reviewer 2 Report
In this paper, the authors propose a method of time-series data-generation that don't have a excessive ajust in their data training with RUL. This is a important challenge in RUL training model using Machine Learning and Deep Learning.
"To the best of our knowledge,...": I think that this sentence is ambiguous to support a research paper. (Section 1. Line 82). By the way, it's difficult to diagnosticate if the results have consistence.
Some papers that maybe are interested to cite or to view. I think that can be interested in this article or another one:
Xie, Z., Du, S., Lv, J., Deng, Y., & Jia, S. (2021). A Hybrid Prognostics Deep Learning Model for Remaining Useful Life Prediction. Electronics, 10(1), 39.
An, D., Choi, J. H., & Kim, N. H. (2018). Prediction of remaining useful life under different conditions using accelerated life testing data. Journal of Mechanical Science and Technology, 32(6), 2497-2507.
Borst, N. G. (2020). Adaptations for CNN-LSTM Network for Remaining Useful Life Prediction: Adaptable Time Window and Sub-Network Training.
Ramasso, E., & Gouriveau, R. (2014). Remaining useful life estimation by classification of predictions based on a neuro-fuzzy system and theory of belief functions. IEEE Transactions on Reliability, 63(2), 555-566.
Only like curiosity, what's the system to recapitulate the information for this article?
On one hand, there is too much technical information. It is not bad, but can to lose the lector's attention. Maybe you can simplify it a little, and add it like an appendix for the people with more interest. The explanation in Figure 4, for example, is really good. More text and visual examples and fewer formulas on the explanation.
Subsection 5.2: "Results" is better than the previous.
Section 6 is not bad. Maybe you could deepen, but is ok.
Graphics are goods.
Author Response
"Please see the attachment."
Author Response File: Author Response.docx
Round 2
Reviewer 2 Report
I think that with changes the results would be better.
However, I can't see the final version, only answers to the questions.
Author Response
We send our revised manuscript as the attached file again because reviewer 2 said that he/she couldn't see the manuscript but satisfying our answers.
Only 1 attached file is allowed, so the revised manuscript except our answers is attached.
Author Response File: Author Response.docx
