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Article
Peer-Review Record

Constructing a Reliable Health Indicator for Bearings Using Convolutional Autoencoder and Continuous Wavelet Transform

Appl. Sci. 2020, 10(24), 8948; https://doi.org/10.3390/app10248948
by Mohammadreza Kaji 1, Jamshid Parvizian 1 and Hans Wernher van de Venn 2,*
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Appl. Sci. 2020, 10(24), 8948; https://doi.org/10.3390/app10248948
Submission received: 23 November 2020 / Revised: 11 December 2020 / Accepted: 11 December 2020 / Published: 15 December 2020
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)

Round 1

Reviewer 1 Report

I do not have anything against the paper, and I feel that the Golden time of wavelet transform for mathematicians was in 1990s and early 2000s. At present, engineers use this tool for data analysis.

I think description of other methods to detect faults in roller bearings is missing in the introduction. Why CWT is better than predictive models or it is just another tool, which authors prefer?

Author Response

Dear reviewer,

please find our comments in the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

The reviewer's comments are detailed in the enclosed document. 

Comments for author File: Comments.pdf

Author Response

Dear reviewer,

please find our comments and revision in the attachement

Author Response File: Author Response.pdf

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