Next Article in Journal
Natural Language Processing and Artificial Intelligence for Enterprise Management in the Era of Industry 4.0
Next Article in Special Issue
Binary Dense SIFT Flow Based Position-Information Added Two-Stream CNN for Pedestrian Action Recognition
Previous Article in Journal
Adsorption of Emerging Pollutant by Pecan Shell-Based Biosorbent
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 12
Issue 18
10.3390/app12189212
Review Report
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
Article
Peer-Review Record

Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios

Appl. Sci. 2022, 12(18), 9212; https://doi.org/10.3390/app12189212
by Francesca Calabrese *, Alberto Regattieri, Marco Bortolini and Francesco Gabriele Galizia
Reviewer 1: Anonymous
Reviewer 2:
Dongsheng Du
Appl. Sci. 2022, 12(18), 9212; https://doi.org/10.3390/app12189212
Submission received: 20 August 2022 / Revised: 8 September 2022 / Accepted: 10 September 2022 / Published: 14 September 2022
(This article belongs to the Special Issue Artificial Intelligence and Complex Systems Analysis in Transportation and Maintenance)

Round 1

Reviewer 1 Report

Very interesting work.

Comments to the authors:

1-      In the section Introduction: the review of the present work is insufficient, which in turn makes the research weak. Other literature on numerical models for the same purpose (or similar) can be added, addressing the assumptions, main challenges and advancements. In the published literature, there are several approaches (supervised and unsupervised) for fault detection. For example, the authors can take a look at these articles:

https://doi.org/10.1080/00423114.2022.2103436

https://doi.org/10.1080/23248378.2022.2096132

2-     Where are the location of sensors 1,2 and 3 in figure 1?

3-     What is the difference between faults 1, and 2 in figure 3?

4-     What is the difference between conditions 1 and 2 in section 6.1?

5-     In line 490 what are setting 3 and 4?

6-     In section 6.5, it is not clear how the authors obtained crest factor, shape factor, kurtosis, …

 

 

Author Response

Please see the attachment

Author Response File: Author Response.docx

Reviewer 2 Report

Comment 1: Abstract part of the redundant description too much, which can be simplified.

 

Comment 2: There are some problems with the image format and title layout, and some parts of the article layout are not suitable, please modify.

 

Comment 3: Please give a general explanation of the formula involved in the feature extraction method mentioned in the paper.

 

Comment 4: In the Experimental design, the specific reasons for faults 1, 2, 3 and 4 are not explained. Please briefly explain what the specific faults are.

 

Comment 5: The figure 43 mentioned on page 6 is nowhere to be found in the article.

 

Comment 6: The time domain features extracted by Pearson correlation analysis mentioned in the conclusion can reveal health and failure conditions. Please explain the specific working principle.

 

Comment 7: Please check the spelling and grammar of English words.

Author Response

Please see the attachment

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

My remarks have been addressed properly. I think, it is OK now and can be published.

Reviewer 2 Report

All the comments have been considered in the revised manuscript, it can be accepted now.

Back to TopTop