Next Article in Journal
Elastomer-Embedded Multiplexed Optical Fiber Sensor System for Multiplane Shape Reconstruction
Next Article in Special Issue
Short-Training Damage Detection Method for Axially Loaded Beams Subject to Seasonal Thermal Variations
Previous Article in Journal
Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning
Previous Article in Special Issue
Degradation Feature Extraction Method for Prognostics of an Extruder Screw Using Multi-Source Monitoring Data
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sensors
Volume 23
Issue 2
10.3390/s23020991
Review Report
sensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

Printed Edition

A printed edition of this Special Issue is available at MDPI Books....
share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
Article
Peer-Review Record

The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy

Sensors 2023, 23(2), 991; https://doi.org/10.3390/s23020991
by Deyu Zhuang 1, Hongrui Liu 1, Hao Zheng 2, Liang Xu 2, Zhengyang Gu 2, Gang Cheng 2 and Jinbo Qiu 1,*
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Sensors 2023, 23(2), 991; https://doi.org/10.3390/s23020991
Submission received: 20 December 2022 / Revised: 5 January 2023 / Accepted: 13 January 2023 / Published: 15 January 2023
(This article belongs to the Special Issue Structural Health Monitoring: Advanced Sensing, Diagnostics and Prognostics)

Round 1

Reviewer 1 Report

This paper aiming at the fault characteristics of rolling bearings, a feature extraction algorithm based on variational modal decomposition and sample entropy has been proposed. Mean- while, an optimal identification method named IBA-ISMO also has been designed. It is interesting. However, it needs to be modified before publishing on Sensors. Some comments can be summarized as follows:

1.     Using the optimization algorithm to optimize the parameters of sample entropy, better parameters can be obtained.

2.     IBA needs to be compared with other optimization algorithms to illustrate the advantages of this algorithm

3.     What are the parameters of the IBA algorithm based on?

4.     It is necessary to compare the performance of IBA and BA algorithm in Rastrigin function and Ack-ley function, so as to show that IBA algorithm is superior to BA algorithm.

5.     Training time and test time, training accuracy and test accuracy should be put together in a clearer way

6.     The feature extracted in this paper should be compared with the traditional fault feature to illustrate the effectiveness of the feature extracted in this paper.

7.     Why not use deep learning or other methods for troubleshooting, and what are the advantages of the methods presented in this article? This needs to be illustrated by comparison with other methods.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

1. The abstract needs to revise and improve in a way that the key idea to the existing challenges be well explained.

2.The clarity of some figures in the text is too low, which makes it difficult to see the effects of different methods ,such as Figure 7, 9.

3.The workload of the experimental link in this paper is too little. It is suggested to compare and analyze the recognition accuracy with other existing methods to highlight the effectiveness of the proposed method.

4.The conclusion of this paper does not well summarize the beneficial results obtained in this paper.

 

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

none

Back to TopTop