Role of Prognostics in Support of Integrated Risk-based Engineering in Nuclear Power Plant Safety

There is a growing trend in applying a prognostics and health management approach to engineering systems in general and space and aviation systems in particular. This paper reviews the role of prognostics and health management approach in support of integrated risk-based applications to nuclear power plants, like risk-based in-service inspection, technical specification optimization, maintenance optimization, etc. The review involves a survey of the state-of-art technologies in prognostics and health management and an exploration of its role in support of integrated risk-based engineering and how the technology can be adopted to realize enhanced safety and operational performance. An integrated risk-based engineering framework for nuclear power plants has been proposed, where probabilistic risk assessment plays the role of identification, prioritization and optimization of systems, structures, and components, while deterministic assessment is performed using a prognostics and health management approach. Keeping in view the requirements of structural reliability assessment, the paper also proposes essential features of a ‘Mechanics-of-Failure’ approach in support of integrated risk-based engineering. The performance criteria used in prognostics and health management has been adopted to meet requirements of risk-based applications.

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Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

Frontiers in Energy Research ◽

10.3389/fenrg.2021.696785 ◽

2021 ◽

Vol 9 ◽

Author(s):

Xingang Zhao ◽

Junyung Kim ◽

Kyle Warns ◽

Xinyan Wang ◽

Pradeep Ramuhalli ◽

...

Keyword(s):

Nuclear Power ◽

Power Plants ◽

Nuclear Power Plants ◽

Health Management ◽

Nuclear Industry ◽

Data Availability ◽

Data Driven ◽

Special Focus ◽

Prognostics And Health Management ◽

Term Operation

In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

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