Demonstration of the validity of the early warning in online monitoring system for nuclear power plants

Min, Ji Ho; Kim, Dong Wook; Park, Chi-Yong

doi:10.1016/j.nucengdes.2019.04.028

Cited by 22 publications

(8 citation statements)

References 6 publications

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“…The wavelet packet energy is the sum of the squares of the wavelet packet coefficients on a single scale. 2…”

Section: A Feature Matrix Extraction Of Input Signalmentioning

confidence: 99%

“…Depending on the fault occurrence and development process, machines faults can be divided into either sudden or gradual faults. In general, gradual faults are detectable and incipient fault prediction technology can provide warning of minor or abnormal equipment symptoms in advance, which enables the factory manager to optimize maintenance tasks and formulate production plan effectively [2]. Thus, incipient fault prediction technology has important engineering application value.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Incipient Fault Prediction for Non-Stationary and Non-Linear Rotating Systems: Methodology, Model Construction and Application

et al. 2020

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Many researches have been carried out on incipient fault prediction technology for key machine components (such as bearings) based on historical and real-time condition monitoring data. However, there is still lack of well-understood systematic methodologies for detecting incipient fault for rotating machines. Based on machine learning technology, this paper studies an incipient fault prediction model applying with wavelet packet decomposition and dynamic kernel principal component analysis (WPD-DKPCA) to meet the needs of engineering applications. The incipient fault prediction WPD-DKPCA model, which does not require knowledge on equipment structure and failure mechanisms, only requires normal state data of the machine, and incipient fault prediction can be achieved through self-learning. Run-to-failure experimental data and engineering case data have been used to verify the constructed model, and the verification results show that the constructed model can reliably and accurately detect an incipient bearing fault. Comparisons of fault prediction effects prove that using T 2 statistic monitoring can detect upcoming faults of machines much earlier than Kurtosis and Root Mean Square (RMS).

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“…The wavelet packet energy is the sum of the squares of the wavelet packet coefficients on a single scale. 2…”

Section: A Feature Matrix Extraction Of Input Signalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Driven Incipient Fault Prediction for Non-Stationary and Non-Linear Rotating Systems: Methodology, Model Construction and Application

et al. 2020

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“…[57] a never-ending learning method (based on dendrograms and 1-nearest-neighbor classifiers) is developed for online diagnosis of different types of faults in a gas turbine oil system operating in dynamically evolving environments. In the nuclear field (which is of particular interest to the present paper), several techniques have been employed for the early identification and diagnosis of accidents in fission systems, including: classical neural network architectures and Bayesian statistics for identifying LOCA events in a pressurized heavy water reactor [58]; deep neural networks for the fault detection and remaining useful life prediction of solenoid operated valves [59] and for online monitoring of the (modular) Integrated Pressurized Water Reactor IP-200 [60]; (Kernel) Principal Component Analysis combined with clustering for anomaly detection and isolation in an advanced heavy water reactor [61] and for spotting pipe ruptures in the cooling system of a pressurized light-water reactor [62]; particle filters embedded with neural networks to detect very small-break LOCAs in pressurized water reactors [63]; Auto-Associative Kernel Regression for early warnings about the water level of a pressurizer, on the moisture separator and reheater temperature transmitters and on environmental influences in real nuclear power plants of the Korea Hydro & Nuclear Power Co., Ltd. (KHNP) (Central Research Institute, KHNP, 70, 1312-gil, Yuseong-daero, Yuseong-gu, Daejeon 34101, Republic of Korea) [64]; Bayesian Networks for the modelbased diagnosis in a single-phase heat exchanger [65]; Support Vector Machines combined with Gaussian Process Regression for the transient analysis of seven different (normal and accidental) conditions (LOCAs, load rejection, steam generator rupture, etc.) in a simulated nuclear plant [66]; incremental learning and reconciliation of different clustering approaches by unsupervised schemes applied to a fleet of nuclear power plant turbines during shut-down transients [67].…”

Section: Introductionmentioning

confidence: 99%

Metamodeling and On-Line Clustering for Loss-of-Flow Accident Precursors Identification in a Superconducting Magnet Cryogenic Cooling Circuit

et al. 2021

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In the International Thermonuclear Experimental Reactor, plasma is magnetically confined with Superconductive Magnets (SMs) that must be maintained at the cryogenic temperature of 4.5 K by one or more Superconducting Magnet Cryogenic Cooling Circuits (SMCCC). To guarantee cooling, Loss-Of-Flow Accidents (LOFAs) in the SMCCC are to be avoided. In this work, we develop a three-step methodology for the prompt detection of LOFA precursors (i.e., those combinations of component failures causing a LOFA). First, we randomly generate accident scenarios by Monte Carlo sampling of the failures of typical SMCCC components and simulate the corresponding transient system response by a deterministic thermal-hydraulic code. In this phase, we also employ quick-running Proper Orthogonal Decomposition (POD)-based Kriging metamodels, adaptively trained to reproduce the output of the long-running code, to decrease the computational time. Second, we group the generated scenarios by a Spectral Clustering (SC) employing the Fuzzy C-Means (FCM), in order to identify the main patterns of system evolution towards abnormal states (e.g., a LOFA). Third, we develop an On-line Supervised Spectral Clustering (OSSC) technique to associate time-varying parameters measured during plant functioning to one of the prototypical groups obtained, which may highlight the related LOFA precursors (in terms of SMCCC components failures). We apply the proposed technique to the simplified model of a cryogenic cooling circuit of a single module of the ITER Central Solenoid Magnet (CSM). The framework developed promptly detects 95% of LOFA events and around 80% of the related precursors.

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“…Welz et al [6] developed the early warning function in online monitoring system for nuclear power plants by using the Auto-Association Kernel Regression (AAKR) technique and present three cases that represent the functions and roles of the system. Min et al [7] investigates the influence of integrating maintenance information of nuclear plant equipment on prognostic model prediction accuracy, and the developed maintenance-dependent models can greatly improve the accuracy.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Long Short-Term Memory Model for Fault Early Warning of Nuclear Power Turbine

Liu

Shen

et al. 2020

IEEE Access

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Fault early warning of equipment in nuclear power plant can effectively reduce unplanned forced shutdown and avoid significant safety accidents. This paper presents a Bayesian Long Short-Term Memory (LSTM) neural network method for fault early warning method of nuclear power turbine. The Long Short-Term Memory neural network prediction model is developed to address data uncertainty while taking into account complicated situation of the equipment operation. Quantitative reliability validation method is established based on Bayesian inference. A wavelet packet multi-scale time-frequency analysis is employed for data denoising. A Probabilistic Principal Component Analysis (PPCA) method combined with key factor analysis is proposed for dimension reduction and dealing with the data uncertainty. The principal component inverse search method is developed to identify the critical factors mainly contributing to the turbine fault. Numerical results indicate that the proposed novel model is validated with Bayesian confidence of 92% by using the real-world steam turbine data and the model can provide accurate warning in the early creep stage of the fault. INDEX TERMS Bayesian inference, long short-term memory, discrete wavelet packet transform, nuclear power turbine, probabilistic principal component analysis.

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Demonstration of the validity of the early warning in online monitoring system for nuclear power plants

Cited by 22 publications

References 6 publications

Data-Driven Incipient Fault Prediction for Non-Stationary and Non-Linear Rotating Systems: Methodology, Model Construction and Application

Data-Driven Incipient Fault Prediction for Non-Stationary and Non-Linear Rotating Systems: Methodology, Model Construction and Application

Metamodeling and On-Line Clustering for Loss-of-Flow Accident Precursors Identification in a Superconducting Magnet Cryogenic Cooling Circuit

Bayesian Long Short-Term Memory Model for Fault Early Warning of Nuclear Power Turbine

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