Potential, challenges and future directions for deep learning in prognostics and health management applications
Descripción general
Resumen del artículo
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.
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Scientists are looking at how super-smart computer brains can help us know when machines will break. It's like teaching a computer to tell you if your toy car needs fixing before it stops working, helping us keep things healthy.
Posibles conflictos de intereses
None identified
Limitaciones identificadas
Explicación de la calificación
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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