A data-driven adaptive fault diagnosis methodology for nuclear power systems based on NSGAII-CNN

Ge, Daochuan; Yang, Minghan; Yong, Nuo; Wang, Qianqian; Yu, Jie

doi:10.1016/j.anucene.2021.108326

Cited by 41 publications

(12 citation statements)

References 27 publications

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“…This difficulty is likely to require significant time and effort to be overcome. An alternative path has therefore recently started to be intensively pursued, which consists in using modern digital techniques such as artificial intelligence (AI)-also used for the analysis of data obtained from materials health monitoring (Section 3.3)-to extract relevant materials features from large amounts of data: so-called (big) data-driven modelling [172,173]. These techniques make the best of the data that can be made available, by identifying complex correlations between, on the one side, the parameters that define the materials or the components (e.g., composition and fabrication features), as well as the exposure conditions (e.g., temperature, exposure time, radiation dose and dose-rate, .…”

Section: Advanced Modelling and Characterisationmentioning

confidence: 99%

Materials for Sustainable Nuclear Energy: A European Strategic Research and Innovation Agenda for All Reactor Generations

Malerba

Mazouzi²,

Bertolus

et al. 2022

Energies

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Nuclear energy is presently the single major low-carbon electricity source in Europe and is overall expected to maintain (perhaps eventually even increase) its current installed power from now to 2045. Long-term operation (LTO) is a reality in essentially all nuclear European countries, even when planning to phase out. New builds are planned. Moreover, several European countries, including non-nuclear or phasing out ones, have interests in next generation nuclear systems. In this framework, materials and material science play a crucial role towards safer, more efficient, more economical and overall more sustainable nuclear energy. This paper proposes a research agenda that combines modern digital technologies with materials science practices to pursue a change of paradigm that promotes innovation, equally serving the different nuclear energy interests and positions throughout Europe. This paper chooses to overview structural and fuel materials used in current generation reactors, as well as their wider spectrum for next generation reactors, summarising the relevant issues. Next, it describes the materials science approaches that are common to any nuclear materials (including classes that are not addressed here, such as concrete, polymers and functional materials), identifying for each of them a research agenda goal. It is concluded that among these goals are the development of structured materials qualification test-beds and materials acceleration platforms (MAPs) for materials that operate under harsh conditions. Another goal is the development of multi-parameter-based approaches for materials health monitoring based on different non-destructive examination and testing (NDE&T) techniques. Hybrid models that suitably combine physics-based and data-driven approaches for materials behaviour prediction can valuably support these developments, together with the creation and population of a centralised, “smart” database for nuclear materials.

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Section: Advanced Modelling and Characterisationmentioning

confidence: 99%

Materials for Sustainable Nuclear Energy: A European Strategic Research and Innovation Agenda for All Reactor Generations

Malerba

Mazouzi²,

Bertolus

et al. 2022

Energies

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“…Among both GASF and GADF [ 26 ], alternatives transforms exist, including Markov transition matrix heatmaps [ 27 ], and Markov transition fields encoding maps [ 28 ]. In this work, authors have chosen GASF for this work due to its balanced availability and support in literature coupled with its untapped potential in enhancing energy data classification performance.…”

Section: Related Workmentioning

confidence: 99%

Lightweight Gramian Angular Field classification for edge internet of energy applications

Alsalemi

Amira²,

Malekmohamadi³

et al. 2022

Cluster Comput

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With adverse industrial effects on the global landscape, climate change is imploring the global economy to adopt sustainable solutions. The ongoing evolution of energy efficiency targets massive data collection and Artificial Intelligence (AI) for big data analytics. Besides, emerging on the Internet of Energy (IoE) paradigm, edge computing is playing a rising role in liberating private data from cloud centralization. In this direction, a creative visual approach to understanding energy data is introduced. Building upon micro-moments, which are timeseries of small contextual data points, the power of pictorial representations to encapsulate rich information in a small two-dimensional (2D) space is harnessed through a novel Gramian Angular Fields (GAF) classifier for energy micro-moments. Designed with edge computing efficiency in mind, current testing results on the ODROID-XU4 can classify up to 7 million GAF-converted datapoints with ~ 90% accuracy in less than 30 s, paving the path towards industrial adoption of edge IoE. Supplementary Information The online version contains supplementary material available at 10.1007/s10586-022-03704-1.

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“…To address this challenge, many new FD approaches based on CNN [18,19] and long short-term memory (LSTM) network have been proposed for the NPP system. He et al [20,21] used Markov to process the multistate data. They transformed the data into color images after flatting the multistate data into onedimensional data and extracted features through the CNN image processing functionality.…”

Section: Introductionmentioning

confidence: 99%

“…To address the above challenges, this paper proposes a data-driven FD method based on time-series analysis. On the one hand, the method uses PCA method to reduce the dimension to exclude some irrelevant features and integrated some related features but not lose much information; on the other hand, the method considers the connection between the features and time series, arranged these data into a matrix, and used the convolution method to extract both partial features and partial time series at the same time, which preserves the structure compared with [20] so that it can realize the FD of NPP system more accurately and efficiently. The method does not have strict requirement on the setting of the parameter values, which makes it have high adaptability and can be applied to different NPP application scenarios.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis to Nuclear Power Plant System Based on Time-Series Convolution Neural Network

Li¹,

Han²,

Dai³

et al. 2022

Wireless Communications and Mobile Computing

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Nuclear power plant (NPP) is a highly complex engineering system which has typical internal feedback and strong component coupling. With these features, most NPP systems have high risk of radioactive release, which makes it essential to perform fault detection (FD) to the NPP systems. To address this challenge, this paper proposes a FD mechanism named characteristic time-series convolutional neural network (CT-CNN) based on principal component analysis (PCA), time-series analysis, and convolutional neural network (CNN) mechanisms. First, the models of NPP FD system are formulated. Then, the PCA mechanism is applied to extract the features of the NPP system. Next, the time-series analysis and CNN approaches are applied to realize FD to the NPP system. With the above mechanisms, the proposed approach has not only shown strong stability and become adaptive to different data set, but also preserves both time and state characteristics of the NPP system. In experiment, it shows the proposed approach can achieve better performance in both detection accuracy and variance than the classic back propagation, LSTM method, and standard CNN algorithms. More significantly, its optimal accuracy can be as high as 99.8%.

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A data-driven adaptive fault diagnosis methodology for nuclear power systems based on NSGAII-CNN

Cited by 41 publications

References 27 publications

Materials for Sustainable Nuclear Energy: A European Strategic Research and Innovation Agenda for All Reactor Generations

Materials for Sustainable Nuclear Energy: A European Strategic Research and Innovation Agenda for All Reactor Generations

Lightweight Gramian Angular Field classification for edge internet of energy applications

Fault Diagnosis to Nuclear Power Plant System Based on Time-Series Convolution Neural Network

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