The Application of LSTM Model to the Prediction of Abnormal Condition in Nuclear Power Plants

She, Jingke; Xue, Shi-Yu; Sun, Peiwei; Cao, Huasong

doi:10.1007/978-981-15-1876-8_46

Cited by 10 publications

(9 citation statements)

References 8 publications

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“…The experimental results show that the LSTM-based model proposed in Ref. [123] is able to effectively predict the changing trend of core parameters under accident conditions. At the same time, in the simulation condition prediction of small Loss Of Coolant Accident (LOCA), the accurate condition trend prediction for the same kind of accident shows the good generalization ability of the LSTM-based method.…”

Section: Sequence Data Processing In Nuclear Industrymentioning

confidence: 97%

“…The proposed autonomous operation algorithm has a superior ability to monitor, control, and diagnose nuclear safety systems. In the accident monitoring, She et al [123] proposed a DL model based on LSTM to predict the abnormal working conditions of the nuclear industry safety system. The proposed model makes full use of the advantages of LSTM for long-time sequence data processing and realizes the prediction of core parameters under abnormal working conditions through historical operation dataset and rolling update training method.…”

Section: Sequence Data Processing In Nuclear Industrymentioning

confidence: 99%

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Deep learning in nuclear industry: A survey

Tang

Gao

et al. 2022

Big Data Min. Anal.

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As a high-tech strategic emerging comprehensive industry, the nuclear industry is committed to the research, production, and processing of nuclear fuel, as well as the development and utilization of nuclear energy.Nowadays, the nuclear industry has made remarkable progress in the application fields of nuclear weapons, nuclear power, nuclear medical treatment, radiation processing, and so on. With the development of artificial intelligence and the proposal of "Industry 4.0", more and more artificial intelligence technologies are introduced into the nuclear industry chain to improve production efficiency, reduce operation cost, improve operation safety, and realize risk avoidance. Meanwhile, deep learning, as an important technology of artificial intelligence, has made amazing progress in theoretical and applied research in the nuclear industry, which vigorously promotes the development of informatization, digitization, and intelligence of the nuclear industry. In this paper, we first simply comb and analyze the intelligent demand scenarios in the whole industrial chain of the nuclear industry. Then, we discuss the data types involved in the nuclear industry chain. After that, we investigate the research status of deep learning in the application fields corresponding to different data types in the nuclear industry. Finally, we discuss the limitation and unique challenges of deep learning in the nuclear industry and the future direction of the intelligent nuclear industry.

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Section: Sequence Data Processing In Nuclear Industrymentioning

confidence: 97%

Section: Sequence Data Processing In Nuclear Industrymentioning

confidence: 99%

Deep learning in nuclear industry: A survey

Tang

Gao

et al. 2022

Big Data Min. Anal.

View full text Add to dashboard Cite

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“…The convolutional computation from CNN, with the assistance of weight sharing and pooling operation, can effectively extract the major features at the early stage of the development. The LSTM model, as a variety of Recurrent Neural Network (RNN), is proficient at dealing with long-time series datasets such as LOCA data (She et al, 2019). Since the LOCA process is hard to predict due to complicated variations, two LSTM layers are used to increase the depth of the neural network.…”

Section: Cnn+lstm For Post-loca Predictionmentioning

confidence: 99%

Diagnosis and Prediction for Loss of Coolant Accidents in Nuclear Power Plants Using Deep Learning Methods

et al. 2021

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A combination of Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and Convolutional LSTM (ConvLSTM) is constructed in this work for the fault diagnosis and post-accident prediction for Loss of Coolant Accidents (LOCAs) in Nuclear Power Plants (NPPs). The advantages of ConvLSTM, such as effective feature determination and extraction, are applied to the classification of LOCA cases. The prediction accuracy is enhanced via the collaborative work of CNN and LSTM. Such a hybrid model is proved to be functional, accurate, and adaptive, offering quick accident judgment and a reliable decision basis for the emergency response purpose. It then allows NPPs to have an Artificial Intelligence (AI)-based solution for fault diagnosis and post-accident prediction.

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“…Due to its superior prediction capabilities, the LSTM algorithm has found many applications in various industries. A study by [28] applied LSTM network in the prediction of water level for pressurizer of a nuclear reactor, abnormal conditions prediction in an NPP by using the LSTM model was presented by [6], and works by [29] utilized LSTM scheme in the prediction of bearing performance degradation and forecasting of wind power while employing LSTM approach [30] are some of the examples of the LSTM algorithm applications.…”

Section: Anfis Parametersmentioning

confidence: 99%

“…Moreover, a deep convolution neural network (DCNN) was optimized by sliding window technique to diagnose faults [5]. LSTM model was applied in NPP to predict abnormal conditions [6]. Works by [7] used LSTM network and Function-Based Hierarchical Framework (FHF) in the autonomous operation of a reference three-loop pressurized water reactor NPP safety systems.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques

Mwaura

Liu

2021

Science and Technology of Nuclear Installations

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Fault diagnosis occurrence and its precise prediction in nuclear power plants are extremely important in avoiding disastrous consequences. The inherent limitations of the current fault diagnosis methods make machine learning techniques and their hybrid methodologies possible solutions to remedy this challenge. This study sought to develop, examine, compare, and contrast three robust machine learning methodologies of adaptive neurofuzzy inference system, long short-term memory, and radial basis function network by modeling the loss of feed water event using RELAP5. The performance indices of residual plots, mean absolute percentage error, root mean squared error, and coefficient of determination were used to determine the most suitable algorithms for accurately diagnosing the loss of feed water transient signatures. The study found out that the adaptive neurofuzzy inference system model outperformed the other schemes when predicting the temperature of the steam generator tubes, the radial basis function network scheme was best suited in forecasting the mass flow rate at the core inlet, while the long short-term memory algorithm was best suited for the estimation of the severities of the loss of the feed water fault.

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The Application of LSTM Model to the Prediction of Abnormal Condition in Nuclear Power Plants

Cited by 10 publications

References 8 publications

Deep learning in nuclear industry: A survey

Deep learning in nuclear industry: A survey

Diagnosis and Prediction for Loss of Coolant Accidents in Nuclear Power Plants Using Deep Learning Methods

Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques

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