Online Prognosis of Bimodal Crack Evolution for Fatigue Life Prediction of Composite Laminates Using Particle Filters

Round 1

Reviewer 1 Report

Dear Editor: I would like to express my deep thanks for inviting me to review the manuscript ID: applsci-1250135 Title: Online Prognosis of Bimodal Crack Evolution for Fatigue Life Prediction of Composite Laminates using Particle Filters Authors: Karkulali Pugalenthi, Pham Luu Trung Duong, Jaehyeok Doh, Shaista Hussain, Mark Hyunpong Jhon and Nagarajan Raghavan Comments: Abstract: Please delete this sentence “Accurate online prognosis for crack propagation phenomena is essential in the field of structural health monitoring (SHM) of several mechanical devices and systems to improve func-tional safety and reliability”. Please rewrite the abstract according to your results. Introduction: There is no sufficient relevant information. Please rewrite the introduction section. Clearly explain the objectives and novelty. Results and discussion: (i) Explain in detail in Figure 1 the damage area increased with cumulative impact energy. (ii) Please change Figure 3 it is not visible. (iii) Please change Figure 4 it is not visible. (iv) Please change the color in Figure 5 like paris-paris model and paris Model. (v) Please change Figure 7 it is not visible. (vi) Please change Figure 8 it is not visible. Conclusion part: Please concise the conclusion parts. RECOMMENDATION After reviewing the enclosed manuscript for “Applied sciences”, the present manuscript contains some kinds of scientific analysis but it is mandatory required to modify according to the preceding remarks. So, the manuscript can be publication after major revision.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

This article presents an online prognostic framework using particle filters for predicting the remaining useful life of GFRP laminates.The authors improvised the methodology presented in /20/and performed an online prognostic study on the same damage growth data. The proposed prognostic framework has the ability to determine the jump energy or the inflection point in real time on-the-fly.

I consider the topic of the paper to be extremely topical. The methods used correspond to the current state in this area. Positively evaluates the presented improved methodology in comparison with the literature / 20 /. I consider the proposed solution to be original and I consider it an important contribution that expands knowledge in the subject area.

I have the following comments on the article:

1. The proposed methodology has not been described clearly enough. An essential element in the proposed methodology is the particle filter, the description of which has not received sufficient attention. The authors cite only one literary source from the 3rd chapter to the Conclusion, namely / 20 /. The considered article is not autonomous in terms of references to used sources from the particle filter area. The authors do not state whether they used the same particle filter implementation algorithms as in / 20 /. Basic articles cited in literature / 20 /, such as / 20 / and / 34 /, should also be included in their bibliography.

        /20/Orchard ME, Vachtsevanos GJ. A particle-filtering approach for on-    line fault diagnosis and faillure prognosls. Trans Inst Meas Control 2009;31(3-4): 221-46.

        /34/Zio E, Peloni G. Particle filtering prognostic estimation of the remaining useful lifeof nonlinear components. Reliab Eng Syst Saf 2011;96(3):403–9.

        2. It is not clear from the article how they came to the data online. Did they use the same technique as in / 20 /? (page 12th-"One of the main reasons for better performance is that the initial damage state is adapted based on the new measurement data available online for prediction")

 

 

 

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Author addressed all of my review comments in revised manuscript.