Deep learning algorithms provide plausible benefits for efficient prediction and analysis of nuclear reactor safety phenomena. However, research works that discuss the critical challenges with deep learning models from the reactor safety perspective are limited. This article presents the state-of-the-art in deep learning application in nuclear reactor safety analysis, and the inherent limitations in deep learning models. In addition, critical issues such as deep learning model explainability, sensitivity and uncertainty constraints, model reliability, and trustworthiness are discussed from the nuclear safety perspective, and robust solutions to the identified issues are also presented. As a major contribution, a deep feedforward neural network is developed as a surrogate model to predict turbulent eddy viscosity in Reynolds-averaged Navier–Stokes (RANS) simulation. Further, the deep feedforward neural network performance is compared with the conventional Spalart Allmaras closure model in the RANS turbulence closure simulation. In addition, the Shapely Additive Explanation (SHAP) and the local interpretable model-agnostic explanations (LIME) APIs are introduced to explain the deep feedforward neural network predictions. Finally, exciting research opportunities to optimize deep learning-based reactor safety analysis are presented. 

Deep learning for safety assessment of nuclear power reactors: Reliability, explainability, and research opportunities

Advances in nuclear power system design and fault-based condition monitoring towards safety of nuclear-powered ships

Accurate estimation and prediction of the State-of-Health (SOH) and Remaining Useful Life (RUL) are fundamental to optimal maintenance strategies formulation for Prognostics and Health Management (PHM) of degraded equipment. However, the performance assessment of health state prognostics and RUL prediction is strongly dependent on the errors and uncertainties in physical measurements, and heterogeneous degradation of equipment in time-varying operating conditions. The objective of the paper is to provide a hybrid prognostic framework that integrates a two-phases clustering scheme and a PF-LSTM learning algorithm based on Particle Filter (PF) and Long Short-Term Memory (LSTM) networks for dynamic classification of SOH and long-term RUL prediction in the absence of future observations. The proposed generic hybrid PF-LSTM prognostic approach is demonstrated and compared with other adaptive learning and machine learning methods such as Unscented Particle Filter (UPF) and Radial Basis Function Network (RBFN) on the degradation modeling and RUL prediction for Lithium-ion batteries. The comparison results show that robust prediction performance can be obtained by the hybrid PF-LSTM prognostic approach with accurate characterization of equipment degradation states based on the integrated subtractive-fuzzy clustering analysis. The more accuracy on prognostic estimations in Probability Density Function (PDF) of prior and posterior distributions of battery capacity and RUL that are achieved by particle filtering can gain extensive insights to predictive maintenance action guide. 

An improved generic hybrid prognostic method for RUL prediction based on PF-LSTM learning

Industry 4.0 is strongly intertwined with big data streaming flows from intelligent sensors and machinery installed in industrial facilities. Failures can disrupt production and lead the supply chain ecosystem to malfunction. Maintenance strategies are necessary to safeguard the continuous operation of production lines, minimize supply chain disruptions, and improve sustainability indicators. Within the context of smart manufacturing, predictive maintenance (PdM) approaches could decrease downtimes, reduce operational costs, and increase productivity, improving system performance and decision-making. The overarching aim of this research is to systematically review state-of-the-art predictive maintenance applications across diverse manufacturing sectors to provide customized insights from academic and operational perspectives, summarized into a comparative decision support map. The study classifies predictive maintenance solutions based on prevailing methodologies, input features, predicted variables, applied algorithms, evaluation metrics, and state-of-the-art software tools per industry sector. The outcomes highlight that data-driven predictive maintenance constitutes a cutting-edge solution with a growing interest in modern manufacturing. Moreover, this research provides insights into the technology readiness of each industrial sector, covering modern areas for PdM implementation, while raising the extant challenges. The proposed multi-sector framework is expected to act as a guiding light for researchers and practitioners towards the development of PdM driven applications in data driven industries. 

Predictive maintenance in Industry 4.0: A systematic multi-sector mapping

Deep learning–based nuclear intelligent fault detection and diagnosis (FDD) methods have been widely developed and have achieved very competitive results with the progress of artificial intelligence technology. However, the pretrained model for diagnosis tasks is hard in achieving good performance when the reactor operation conditions are updated. On the other hand, retraining the model for a new data set will waste computing resources. This article proposes an FDD method for cross-condition and cross-facility tasks based on the optimized transferable convolutional neural network (CNN) model. First, by using the pretrained model's prior knowledge, the model's diagnosis performance to be transferred for source domain data sets is improved. Second, a model-based transfer learning strategy is adopted to freeze the feature extraction layer in a part of the training model. Third, the training data in target domain data sets are used to optimize the model layer by layer to find the optimization model with the transferred layer. Finally, the proposed comprehensive simulation platform provides source and target cross-condition and cross-facility data sets to support case studies. The designed model utilizes the strong nonlinear feature extraction performance of a deep network and applies the prior knowledge of pretrained models to improve the accuracy and timeliness of training. The results show that the proposed method is superior to achieving good generalization performance at less training epoch than the retraining benchmark deep CNN model.

https://www.frontiersin.org/articles/10.3389/fenrg.2022.823395/pdf

Model-Based Deep Transfer Learning Method to Fault Detection and Diagnosis in Nuclear Power Plants

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.

/pdf/prognostics-and-health-management-in-nuclear-power-plants-an-4jkdy10w3k.pdf

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

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Michael Golay

Author Tools

Chat about Author

Papers

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