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Physical Sciences › Engineering › Control and Systems Engineering
Potential, challenges and future directions for deep learning in prognostics and health management applications
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Conflicts of Interest
Identified Weaknesses
Rating Explanation
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Paper Summary
Paperzilla title
Deep Learning for PHM: A Promising Future with Challenges Ahead
This paper reviews the potential, challenges, and future directions of deep learning in prognostics and health management (PHM). It highlights the opportunities and difficulties of applying deep learning to PHM applications, discussing aspects such as data processing, model development, and practical considerations for industrial settings.
Possible Conflicts of Interest
None identified
Identified Weaknesses
Lack of Quantitative Results
The paper lacks quantitative results and mainly focuses on a qualitative overview of the field.
Limited Novel Contributions
The paper focuses primarily on reviewing existing literature rather than presenting novel research findings.
Lack of Implementation Details and Validation
While the paper identifies challenges and potential solutions, it doesn't delve into specific implementation details or empirical validation.
Rating Explanation
This paper provides a comprehensive and timely overview of deep learning in PHM, addressing key challenges and potential solutions. While it lacks novel research or quantitative results, its thorough review and insightful discussion make it a valuable contribution to the field. Thus, it is rated as a 4.
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File Information
Original Title:
Potential, challenges and future directions for deep learning in prognostics and health management applications
File Name:
1-s2.0-S0952197620301184-main.pdf
[download]
File Size:
1.06 MB
Uploaded:
July 14, 2025 at 10:36 AM
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