Deep-learning-based alarm system for accident diagnosis and reactor state classification with probability value

Park, Jae Kwan; Lee, Byeong Hee; Seong, Seung Hwan

doi:10.1016/j.anucene.2019.07.022

Cited by 21 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lin et al (2021) Another common trend, both in the literature surrounding ANNs for diagnostics and AI as a whole, is the increased interest in utilizing DL methods. In application to NPP fault diagnostics, DL architectures have been applied by Ahmed et al (2017), Mandal et al (2017), Peng et al (2018a), and Kim et al (2019). Outside of the nuclear industry, Yu et al (2018) applied DL for fault diagnosis in wind turbines, and Ren et al (2019) developed a DL diagnoser in autonomous vehicles.…”

Section: Data-driven 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

View full text Add to dashboard Cite

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.

show abstract

Section: Data-driven 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

View full text Add to dashboard Cite

show abstract

“…The Dirichlet BC (such as zero-flux boundary) is commonly used in reactor physics. In Figure 3, the neutron flux on the physical boundaries is set as φ c (m, n, s), which is only defined on the boundary and can be described in Equations ( 9) and (10) for square boundary (SB) and circular boundary (CB), respectively. SB :…”

Section: Trial Functions For Special Bcs In Bdmmentioning

confidence: 99%

“…The deep learning (DL) method, due to its powerful ability to discover complex structures in large data set and its low human intervene requirements, has attracted many attentions for engineering problems in recent years [8][9][10][11][12]. The DL method has produced encouraging results in many applications of various disciplines, including language processing [13][14][15][16], image recognition [17][18][19], speech recognition [20,21] and finance [22].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Deep Learning Method for Multi-Dimensional Neutron Diffusion Problems with Novel Treatment to Boundary

Xie¹,

Wang²,

Ma³

et al. 2021

JNE

View full text Add to dashboard Cite

In this paper, the artificial neural networks (ANN) based deep learning (DL) techniques were developed to solve the neutron diffusion problems for the continuous neutron flux distribution without domain discretization in advance. Due to its mesh-free property, the DL solution can easily be extended to complicated geometries. Two specific realizations of DL methods with different boundary treatments are developed and compared for accuracy and efficiency, including the boundary independent method (BIM) and boundary dependent method (BDM). The performance comparison on analytic benchmark indicates BDM being the preferred DL method. Novel constructions of trial function are proposed to generalize the application of BDM. For a more in-depth understanding of the BDM on diffusion problems, the influence of important hyper-parameters is further investigated. Numerical results indicate that the accuracy of BDM can reach hundreds of times higher than that of BIM on diffusion problems. This work can provide a new perspective for applying the DL method to nuclear reactor calculations.

show abstract

“…For this, in the early stages of development, artificial neural networks (ANNs) [ 17 ], neuro-fuzzy networks [ 18 , 19 , 20 ], and knowledge-based expert systems [ 21 ] were suggested. More recently, the rapid growth of neural networks has improved the diagnostic performance of the algorithms, with fine performances seen from deep neural networks [ 22 ], recurrent neural networks (RNNs) [ 23 ], and convolutional neural networks (CNNs) [ 24 ]. Additionally, hidden Markov models, pattern recognition, and Bayesian belief networks have also been used to identify accident types [ 25 , 26 , 27 ].…”

Section: Accident Diagnosis In An Emergency Situationmentioning

confidence: 99%

A Sensor Fault-Tolerant Accident Diagnosis System

Choi

Lee

2020

Sensors

View full text Add to dashboard Cite

Emergency situations in nuclear power plants are accompanied by an automatic reactor shutdown, which gives a big task burden to the plant operators under highly stressful conditions. Diagnosis of the occurred accident is an essential sequence for optimum mitigations; however, it is also a critical source of error because the results of accident identification determine the task flow connected to all subsequent tasks. To support accident identification in nuclear power plants, recurrent neural network (RNN)-based approaches have recently shown outstanding performances. Despite the achievements though, the robustness of RNN models is not promising because wrong inputs have been shown to degrade the performance of RNNs to a greater extent than other methods in some applications. In this research, an accident diagnosis system that is tolerant to sensor faults is developed based on an existing RNN model and tested with anticipated sensor errors. To find the optimum strategy to mitigate sensor error, Missforest, selected from among various imputation methods, and gated recurrent unit with decay (GRUD), developed for multivariate time series imputation based on the RNN model, are compared to examine the extent that they recover the diagnosis accuracies within a given threshold.

show abstract

Deep-learning-based alarm system for accident diagnosis and reactor state classification with probability value

Cited by 21 publications

References 8 publications

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

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

Neural Network Based Deep Learning Method for Multi-Dimensional Neutron Diffusion Problems with Novel Treatment to Boundary

A Sensor Fault-Tolerant Accident Diagnosis System

Contact Info

Product

Resources

About