Novel fault diagnosis scheme utilizing deep learning networks

Saeed, Hanan A.; Peng, Minjun; Wang, Hang; Zhang, Bowen

doi:10.1016/j.pnucene.2019.103066

Cited by 58 publications

(10 citation statements)

References 18 publications

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“…At present, the data-driven machine learning (DDML) methods, including the neural network, support vector machine (SVM), dimension reduction learning (DRL), ensemble learning (EL) or random tree (RT), regression approaches, and so on, have been applied to predict the NPP behaviors (Jamil et al, 2016;Saeed et al, 2020). Nevertheless, few researches concern with the state-of-the-art progress and future trends for both the DDML approach for the FDD and the NPP (Bartlett and Uhrig, 1992;Ma and Jiang, 2011;Moshkbar-Bakhshayesh and Ghofrani, 2013).…”

Section: Data-driven Machine Learning Methodsmentioning

confidence: 99%

“…However, there are either the specified component (system) or the outdated techniques in the available research. As the DDML techniques in the NPP sharp a lot in recent years (Rezaeianjouybari and Shang, 2020;Yao et al, 2020;Saeed et al, 2020), there exists a gap in the current state-of-the-art of the DDML techniques for the FDD in the NPP. In this review, the current classifications, principles, characteristics, and applications of the FDD in the NPP, followed by the discussion on the future development of the DDML method for the NPP state prediction, will be illustrated.…”

Section: Data-driven Machine Learning Methodsmentioning

confidence: 99%

“…The DNN technique has been proposed for the transient detection (Mo et al, 2007), PHM (Rezaeianjouybari and Shang, 2020), fault state detection of the solenoid operated valves (Utah and Jung, 2020), and the novel fault scheme (Saeed et al, 2020) in the NPP. In addition, Chae et al (2020) combined the longshort term memory (LSTM) network with the SVM and CNN approach to diagnose the pipe corrosion in the NPP.…”

Section: Deep Neural Network Techniquementioning

confidence: 99%

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Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Zhou

Liu

2021

Front. Energy Res.

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Data-driven machine learning (DDML) methods for the fault diagnosis and detection (FDD) in the nuclear power plant (NPP) are of emerging interest in the recent years. However, there still lacks research on comprehensive reviewing the state-of-the-art progress on the DDML for the FDD in the NPP. In this review, the classifications, principles, and characteristics of the DDML are firstly introduced, which include the supervised learning type, unsupervised learning type, and so on. Then, the latest applications of the DDML for the FDD, which consist of the reactor system, reactor component, and reactor condition monitoring are illustrated, which can better predict the NPP behaviors. Lastly, the future development of the DDML for the FDD in the NPP is concluded.

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Section: Data-driven Machine Learning Methodsmentioning

confidence: 99%

Section: Data-driven Machine Learning Methodsmentioning

confidence: 99%

Section: Deep Neural Network Techniquementioning

confidence: 99%

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Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Zhou

Liu

2021

Front. Energy Res.

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“…Several publications proposed studies on solving partial differential equation [1,2] and in the area of mechanics [3] and fluid dynamics [4], the deep learning approach was also discussed. The deep learning was also applied in the area of nuclear engineering with fault diagnosis and safety analysis [5].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Based Surrogate Model for Estimating the Flux and Power Distribution Solved by Diffusion Equation

Zhang

Liang

et al. 2021

EPJ Web Conf.

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A deep learning based surrogate model is proposed for replacing the conventional diffusion equation solver and predicting the flux and power distribution of the reactor core. Using the training data generated by the conventional diffusion equation solver, a special designed convolutional neural network inspired by the FCN (Fully Convolutional Network) is trained under the deep learning platform TensorFlow. Numerical results show that the deep learning based surrogate model is effective for estimating the flux and power distribution calculated by the diffusion method, which means it can be used for replacing the conventional diffusion equation solver with high efficiency boost.

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“…A common theme in the literature sees ANNs working in tandem with some other technique to transform sensory data into a form usable by the ANN. As examples,Messai et al (2015) andTagaris et al (2019) both used data from wavelet transformations;Lee et al (2021) transformed the number of plant state variables into a 2D image and used a convolutional neural network (CNN) to process the image as a means of diagnosing abnormal states;Saeed et al (2020) implemented a long short-term memory (LSTM) network and CNN after performing PCA; and tested the effectiveness of an RBF network and an Elman neural network (ENN) after using PCA to perform noise filtering for NPP fault diagnosis.…”

mentioning

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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Novel fault diagnosis scheme utilizing deep learning networks

Cited by 58 publications

References 18 publications

Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

A Deep Learning Based Surrogate Model for Estimating the Flux and Power Distribution Solved by Diffusion Equation

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

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