Development and Assessment of Physics-guided Machine Learning for Prognosis System

Lin, Li; Dinh, N.; Gürgen, Ayşenur

doi:10.13182/t123-33503

Cited by 1 publication

(1 citation statement)

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“…The selection of the actual model and method is usually driven by the problem and specific to the application. As an example in nuclear systems, Gurgen et al (2020) recently proposed a physics-guided RNN (with LSTM blocks) prognostic model within the NAMAC system to predict the evolution of fuel centerline temperature in loss of flow transient conditions and demonstrated its superior performance over pure data-driven prognosis. In other fields, research related to the hybrid approach has been much more active (Liao and Kottig, 2014;Atamuradov et al, 2017).…”

Section: Hybrid Methodsmentioning

confidence: 99%

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

et al. 2021

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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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Section: Hybrid Methodsmentioning

confidence: 99%