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 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
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