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Thank you for submitting your manuscript to Advanced Engineering Informatics. I regret to inform you that your paper is not acceptable for publication. We have completed the review of your manuscript and a summary is appended below. The reviewers have advised against publication of your manuscript and I must therefore reject it at this time. For your information and guidance, any specific comments explaining why I have reached this decision and those received from reviewers, if available, are listed at the end of this letter.
You have the option of resubmitting a substantially revised version of your paper, which would be considered as a new submission. If you decide to do this, you should refer to the reference number of the current paper and include a cover letter which explains in detail how the paper has been changed or not, in reply to the Editor and Reviewer comments.
Thank you for giving us the opportunity to consider your work.
Kind regards,
Comments from the editors and reviewers:
-Reviewer 1
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This paper propose the fault diagnosis of rotating machinery components using deep kernel extreme learning machine under variable working conditions. The authors claim that experiment results show that DK-ELM yields more promising results than other state-of-the-art algorithms in terms of diagnosis accuracy and adaptation ability to varying working conditions. The research issue is interesting, and the paper is well organized and written in general. Some of my suggestions are mentioned in below.
1. This paper focuses on the fault diagnosis of rotating machinery components. However, this topic has been discussed for many years. The innovation and value of this research should be strengthened.
2. The abstract only illustrates the concept of research and does not summarize the core values ??and quantitative benefits of propose deep kernel extreme learning machine.
3. In the related works section, the authors only briefly explain each extreme learning machine. The authors should add more number of up to date references, and provide high level comparison with other existing related research works. It is extremely important to summarize the scientific contributions of this research work.
4. The deep kernel extreme learning machine proposed in this study are few to explain, the authors should fully explain the main contents.
5. In Fig.2, the functions of each component should be described in detail, especially in multiple hidden layer and kernel layer.
6. In effect of discriminative manifold regularization term, the authors should compare the experimental results with quantitative data (Fig.4 and Fig.9).
-Reviewer 2
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Based on the following comments, this paper is not ready for publication.
My major concern is that this paper does not fit to the scope of the journal. What is the contribution to the advanced informatics?
Need more relevant and broader references. For example, the following statement do not have relevant reference.
¡°Secondly, back-propagation (BP) training algorithm has some inherent problems such as local minima and over-fitting, which affects the algorithm¡¯s diagnostic accuracy and generalization performance.¡±
Some sections are too long. Consider to make it concise. For Introduction, consider it move some sentences to Literature Review.
The contribution of the article (in the context of fault diagnosis and engineering informatics) should be highlighted more in Introduction.
Were the ELM compared with other learning methods (including ANN, SVM)?
How SDAE [18], DBN [19], ML-ELM [37], ML-GELM [39], KELM [29] and BPNN were selected for the comparison? Need a justification.
Some figures are not readable.
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