Knowledge base operator support system for nuclear power plant fault diagnosis

Ayodeji, Abiodun; Liu, Yong-kuo; Xia, Hong

doi:10.1016/j.pnucene.2017.12.013

Cited by 76 publications

(24 citation statements)

References 9 publications

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“…For instance, Hadad et al (2011) performed a linear regression analysis to evaluate the network performance in the NPP. In 2018, Ayodeji et al (2018) combined the logistic regression with the SVM for the incipient fault diagnosis in the NPP. In particular, the regression algorithm is direct and fast while it also needs to handle the abnormal value.…”

Section: Regression Algorithmmentioning

confidence: 99%

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: Regression Algorithmmentioning

confidence: 99%

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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“…Reference [24] developed a dynamic model of the drum-boiler using NARX neural networks, which can forecast the actual pressure and water level of the drumboiler. Reference [25] proposed a pilot program aiming at developing a comprehensive knowledge base for power plants by using principal component analysis and artificial neural networks. e program used the principal component analysis method to filter noise in the prediagnosis stage and evaluated the neural network model based on representative data of the power plant.…”

Section: Optimization Of the Coal Energy Utilizationmentioning

confidence: 99%

The Bidirectional Information Fusion Using an Improved LSTM Model

Zheng

Wang

Guo

et al. 2021

Mobile Information Systems

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The information fusion technology is of great significance in intelligent systems. At present, the modern coal-fired power plant has the fully functional sensor network. However, many data that are important for the operation of a power plant, such as the coal quality, cannot be directly obtained. Therefore, the information fusion technology needs to be introduced to obtain the implied information of the power plant. As a practical application, the soft measurement of coal quality is taken as the research object. This paper proposes an improved LSTM model combined with the bidirectional deep fusion, alertness mechanism, and parameter self-learning (DFAS-LSTM) to realize online soft computing for the coal quality analyses of industries and elements. First, a latent structure model is established to preprocess the noisy and redundant sensor network data. Second, an alertness mechanism is proposed and the self-learning method of the activation function parameters is used for the data feature extraction. Third, a deeply bidirectional fusion layer is added to the long short-term memory neural network model to solve the problem of the insufficient accuracy and the weak generalization. Using the historical data of the sensor network, the DFAS-LSTM model is established. Then, the online data of the sensor network is input to the DFAS-LSTM model to implement the online coal quality analyses. Experiment shows that the accuracy of the coal quality analyses is increased by 1%–2.42% compared to the traditionally bidirectional LSTM.

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“…Because the gear fault signal is nonlinear and non-stationary, and the Elman-NN has strong nonlinear mapping and fault tolerance, it is especially suitable for nonlinear pattern recognition and classification, so it is introduced into fault diagnosis. Ayodeji [4] introduced an Elman-NN to perform nuclear power plant fault diagnosis, and the results show that the diagnosis method is feasible. Chemseddine [5] proposed a novel fault diagnosis method for gearbox system based on the fusion of HEWT-SVD and Elman-NN.…”

Section: Introductionmentioning

confidence: 99%

Gearbox fault diagnosis method based on the fusion of EEMD and improved Elman-NN

Zhang¹

2021

Vib. proced.

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In connection with the complex operating conditions of gearbox, multiple vibration excitation sources, and difficulty in extracting vibration signal fault features, a novel method of gearbox fault diagnosis is proposed. based on the fusion of EEMD and improved Elman neural network (Elman-NN) is developed. The wavelet packet is utilized to denoise the collected vibration signals of four different types of gearboxes: broken teeth, cracks, wear, and normal, and then use the EEMD method to decompose the denoised vibration signals, and use the correlation coefficient criterion means to carry out the IMF pseudo component elimination, and then get a more effective signal. Calculate the energy feature of the effective signal and use it as the enter feature of the Elman-NN. Based on standard Elman-NN, a self-feedback factor is added to construct a reformed Elman-NN. Experimental results indicate that compared with the standardized Elman-NN, the improved Elman-NN has higher diagnostic accuracy and diagnostic efficiency.

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Knowledge base operator support system for nuclear power plant fault diagnosis

Cited by 76 publications

References 9 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

The Bidirectional Information Fusion Using an Improved LSTM Model

Gearbox fault diagnosis method based on the fusion of EEMD and improved Elman-NN

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