Research Topic

Nuclear Power Plant Equipment Prognostics and Health Management Based on Data-driven methods

About this Research Topic

Currently, in response to the fierce competition in the energy market, nuclear power companies are considering operating nuclear power plants in a more economical, efficient, and safe manner. Besides, with the upgrading of nuclear power plants, systems and equipment are becoming more sophisticated and ...

Currently, in response to the fierce competition in the energy market, nuclear power companies are considering operating nuclear power plants in a more economical, efficient, and safe manner. Besides, with the upgrading of nuclear power plants, systems and equipment are becoming more sophisticated and expensive, which poses challenges to the timeliness, accuracy, and forward-looking of operation and maintenance (O&M) practices. Traditional O&M practices with periodic maintenance as the core need to be further upgraded to meet these requirements.

As a novel O&M strategy, data-driven health management of nuclear power plant equipment is gaining more and more attention. On the one hand, the digitization of nuclear power plants provides a rich source of data. On the other hand, the development of data science and technology, especially the development of big data technology and artificial intelligence technology represented by machine learning and deep learning, provides technical means for efficiently mining and learning laws and knowledge from data.

This Research Topic will explore the application of the latest technical means such as big data, artificial intelligence, deep learning, etc. for the prognostics and health management of crucial equipment of nuclear power plants. This Research Topic includes but is not limited to the following themes:

1. Advanced sensor technology
2. The data-driven approach in condition monitoring
3. The data-driven approach in fault diagnosis
4. The data-driven approach in life prediction
5. The data-driven approach in the maintenance plan and decision making


Keywords: data-driven, artificial intelligence, condition monitoring, fault diagnosis, life prediction


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Recent Articles

Loading..

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

Topic Editors

Loading..

Submission Deadlines

16 December 2020 Manuscript
08 February 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..

Topic Editors

Loading..

Submission Deadlines

16 December 2020 Manuscript
08 February 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..
Loading..

total views article views article downloads topic views

}
 
Top countries
Top referring sites
Loading..