Online fault monitoring based on deep neural network & sliding window technique

Abstract Nuclear power plants have proved their worth in energy sector by providing clean and uninterrupted power over decades. However, a Nuclear Power Plant (NPP) is a complex, dynamic system with potential radioactive release risk which makes it crucial to achieve highest standards of safety. Specially, in preview of massive monitoring data received in modern NPPs which makes it difficult for operators to extract vital information about actual plant state in a timely and accurate manner. On the other hand, advancements in latest machine learning methods have made it possible to process such massive data for operators to act accordingly. However, current machine learning approaches cited for this field, fall short of required capabilities needed for such safety critical industry. In manuscript, an online fault monitoring system is proposed which utilizes deep neural networks and sliding window technique. The proposed model not only fulfills the requirement of validity but also encompass all necessary diagnosis functions like detection, identification, assessment and robustness. The model allows for a fault to be identified and assessed in different plant states and then validate the predicted results through online correlation of simulation vs original data. The study was conducted for IP-200 NPP utilizing RELAP5 thermal-hydraulic code. The proposed model was verified by inducing 04 different faults for different states and severities. The results were found to be conducive for improving reliability and accuracy of next generation fault monitoring systems of Nuclear Power Plants.

[1]  Moncef Gabbouj,et al.  Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Industrial Electronics.

[2]  Ruqiang Yan,et al.  Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis , 2017, IEEE Transactions on Industrial Informatics.

[3]  Yixiang Huang,et al.  Fault Diagnosis of Asynchronous Motors Based on LSTM Neural Network , 2018, 2018 Prognostics and System Health Management Conference (PHM-Chongqing).

[4]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[5]  Tenglong Cong,et al.  Simulation analysis of an open natural circulation for the passive residual heat removal in IPWR , 2018, Annals of Nuclear Energy.

[6]  Moongu Jeon,et al.  Adaptive Sliding-Window Strategy for Vehicle Detection in Highway Environments , 2016, IEEE Transactions on Intelligent Transportation Systems.

[7]  Ming Zhao,et al.  A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox , 2017 .

[8]  A. C. Cilliers,et al.  Continuous machine learning for abnormality identification to aid condition-based maintenance in nuclear power plant , 2018, Annals of Nuclear Energy.

[9]  N. Saad,et al.  Overview of data store management for sliding-window learning using MLP networks , 2012, 2012 4th International Conference on Intelligent and Advanced Systems (ICIAS2012).

[10]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[11]  Minjun Peng,et al.  Numerical Study on Coolant Flow Distribution at the Core Inlet for an Integral Pressurized Water Reactor , 2017 .

[12]  P. Baldi,et al.  Searching for exotic particles in high-energy physics with deep learning , 2014, Nature Communications.

[13]  Ruqiang Yan,et al.  A sparse auto-encoder-based deep neural network approach for induction motor faults classification , 2016 .

[14]  Nils Bausch,et al.  A study on the robustness of neural network models for predicting the break size in LOCA , 2018, Progress in Nuclear Energy.

[15]  Gu Yuhai,et al.  Research on Failure Prediction Using DBN and LSTM Neural Network , 2018, 2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE).

[16]  Peng Minjun,et al.  A cascade intelligent fault diagnostic technique for nuclear power plants , 2018 .

[17]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[18]  Minjun Peng,et al.  Analysis of natural circulation operational characteristics for integrated pressurized water reactor , 2016 .

[19]  Yong-kuo Liu,et al.  Support vector ensemble for incipient fault diagnosis in nuclear plant components , 2018, Nuclear Engineering and Technology.

[20]  Krzysztof Patan,et al.  Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes , 2008 .

[21]  Bo Jin,et al.  Sequential Fault Diagnosis Based on LSTM Neural Network , 2018, IEEE Access.

[22]  Yong-kuo Liu,et al.  Knowledge base operator support system for nuclear power plant fault diagnosis , 2018 .

[23]  Genglei Xia,et al.  Operation characteristic of Integrated Pressurized Water Reactor under coordination control scheme , 2015 .

[24]  Dong Yu,et al.  Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[25]  Minjun Peng,et al.  Novel fault diagnosis scheme utilizing deep learning networks , 2020 .

[26]  Guanghui Su,et al.  Thermal–hydraulic performance analysis of IPWR during full pressure start-up mode , 2013 .

[27]  Dan Guo,et al.  Research on intelligent fault diagnosis method for nuclear power plant based on correlation analysis and deep belief network , 2018, Progress in Nuclear Energy.

[28]  Jun Lu,et al.  Sensor Fault Diagnosis of Autonomous Underwater Vehicle Based on LSTM , 2018, 2018 37th Chinese Control Conference (CCC).

[29]  Li Deng,et al.  A tutorial survey of architectures, algorithms, and applications for deep learning , 2014, APSIPA Transactions on Signal and Information Processing.

[30]  Guanghui Su,et al.  Prediction of LBB leakage for various conditions by genetic neural network and genetic algorithms , 2017 .

[31]  Hyeonmin Kim,et al.  Smart support system for diagnosing severe accidents in nuclear power plants , 2018 .

[32]  Jin Jiang,et al.  Applications of Fault Diagnosis in Nuclear Power Plants: An Introductory Survey , 2009 .

[33]  Belle R Upadhyaya,et al.  A hybrid fault diagnosis methodology with support vector machine and improved particle swarm optimization for nuclear power plants. , 2019, ISA transactions.

[34]  Yuyun Zeng,et al.  Machine learning based system performance prediction model for reactor control , 2018 .