Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks

Santosh, T.V.; Srivastava, A. K.; Rao, Vishal Govind; Ghosh, A.K.; Kushwaha, H.S.

doi:10.1016/j.ress.2008.08.005

Cited by 65 publications

(30 citation statements)

References 6 publications

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“…In the case of non-destructive ultrasonic inspections a logit model for the observation   j fT can be introduced [Simola et al, 1998]:…”

Section: The Model Of the Fatigue Crack Growth Processmentioning

confidence: 99%

“…With respect to AI techniques, the most commonly used prediction methods are based on Neural Networks [Peel et al, 2008;Barlett et al, 1992;Santosh et al, 2009]. For prognostic tasks, 4 promising methods are Recurrent Neural Networks (RNNs) [Campolucci et al, 1999], Neuro-Fuzzy (NF) systems [Wang et al, 2004] and Support Vector Machines (SVMs) [Sotiris et al, 2007].…”

Section: Introductionmentioning

confidence: 99%

“…The adoption of a fuzzy definition of pattern similarity allows capturing and integrating the possibly imprecise information carried by the measured degradation signal. An application is presented with reference to a non linear fatigue crack growth process, typical for a certain class of industrial and structural components [Oswald et al, 1984;Sobezyk et al, 1992;Bolotin et al, 1998;Myotyri et al, 2006].…”

Section: Introductionmentioning

confidence: 99%

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Failure Prognostics by a Data-Driven Similarity-Based Approach

Maio

Zio

2013

Int. J. Rel. Qual. Saf. Eng.

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This paper presents a data-driven, similarity-based approach for prognostics of industrial and structural components. The potentiality of the approach is demonstrated on a problem of crack propagation, taken from literature. The crack growth process is described by a non linear model affected by non-additive noises. A comparison is provided with a Monte Carlo-based estimation method, known as particle filtering.

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“…In the case of non-destructive ultrasonic inspections a logit model for the observation   j fT can be introduced [Simola et al, 1998]:…”

Section: The Model Of the Fatigue Crack Growth Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Failure Prognostics by a Data-Driven Similarity-Based Approach

Maio

Zio

2013

Int. J. Rel. Qual. Saf. Eng.

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“…In the former case, Artificial Intelligence techniques, e.g., Artificial Neural Networks (ANNs) [14][15], similaritybased regression [16][17], etc., are used to directly map the relation between the observation and the equipment RUL. However, when the observations collected are directly related to the equipment degradation state, regression is used to extrapolate the future degradation path and compare it to a failure criterion.…”

Section: Introductionmentioning

confidence: 99%

A prognostics approach to nuclear component degradation modeling based on Gaussian Process Regression

Baraldi

Mangili

Zio

2015

Progress in Nuclear Energy

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Abstract-Advanced diagnostics and prognostics tools are expected to play an important role in ensuring safe and long term operation in nuclear power plants. In this context, we use Gaussian Process Regression (GPR) to build a stochastic model of the equipment degradation evolution and apply it for prognostics.GPR is a probabilistic technique for non-linear nonparametric regression that estimates the distribution of the future equipment degradation states by constraining a prior distribution to fit the available training data, based on Bayesian inference. Training data are taken from sequences of degradation measures collected from a set of similar historical equipments which have undergone a similar degradation process. Given new degradation measures from a currently degrading equipment (test trajectory), the distribution of the Remaining Useful Life (RUL) before failure is estimated by comparing with a failure criterion the distribution of the future degradation states predicted by GPR.Applications are shown on simulated data concerning the evolution of creep damage in ferritic steel exposed to high stress and on real data concerning the clogging of sea water filters placed upstream the heat exchangers of a BWR condenser.Index Terms-Remaining Useful Life, Prediction, Prognostics, Bayesian Inference, Gaussian Process Regression, Degradation, Ccreep, Ffilter Cclogging I. INTRODUCTIONUCLEAR INDUSTRY is considering the development and use of advanced diagnostic and prognostic tools to enable longer term safe operation of nuclear structures, systems and components (SSCs) [1][2][3][4][5][6][7]. This interest is mainly originated by the following three reasons:  many nuclear SSCs are becoming old due to the extension of the life of the existing reactors beyond the initial 30 or 40 years. This aging may cause degradation and even failures if SSC maintenance and replacement is not properly planned [8];  many utilities are considering performing power up-rates in their plants. This typically requires increased coolant flow which may cause faster degradation of the SSCs;  there is a strong economic interest towards having longer fuel cycles and decreased outage times. This may pose constraints on the frequency and extent of in-service inspections which may become not able to detect in a timely manner all SSC degradation modes. In this context, in the present paper, we consider the development of prognostic methods for estimating the remaining useful life of nuclear components, structures and systems. Data-driven and model-based methods can be used for predicting the Remaining Useful Life (RUL) of degrading equipment [9-10], i.e., the remaining time during which the equipment can continue performing its function in a safe and efficient way. This allows the implementation of predictive maintenance strategies which have the potential of increasing safety and lowering costs [11]. Model-based methods assume that a mathematical model of the degradation process is available. In practice, the detailed knowledge necessary for b...

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“…Artificial Neural Network (ANN), Support Vector Machine (SVM), Genetic Algorithm (GA) and Auto-Associative Kernel Regression (AAKR) are among some of the most studied and applied (Chevalier et al, 2009;Baraldi et al, 2010;Baradi et al, 2011;Santosh et al, 2009;Li et al, 2012;Yazikov et al, 2012;Rand et al, 2012a;Rand et al, 2012b;Muralidharan and Sugumaran, 2012;Ekici, 2012;Zio and Gola, 2006;Lu and Upadhyaya, 2005;Jeong et al, 2003;Zio et al, 2009). These approaches are already mature, especially for detection and diagnostics.…”

Section: Introductionmentioning

confidence: 99%

Nuclear power plant components condition monitoring by probabilistic support vector machine

Liu

Seraoui²,

Vitelli

et al. 2013

Annals of Nuclear Energy

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In this paper, an approach for the prediction of the condition of Nuclear Power Plant (NPP) components is proposed, for the purposes of condition monitoring. It builds on a modified version of the Probabilistic Support Vector Regression (PSVR) method, which is based on the Bayesian probabilistic paradigm with a Gaussian prior. Specific techniques are introduced for the tuning of the PSVR hyerparameters, the model identification and the uncertainty analysis. A real case study is considered, regarding the prediction of a drifting process parameter of a NPP component.

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Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks

Cited by 65 publications

References 6 publications

Failure Prognostics by a Data-Driven Similarity-Based Approach

Failure Prognostics by a Data-Driven Similarity-Based Approach

A prognostics approach to nuclear component degradation modeling based on Gaussian Process Regression

Nuclear power plant components condition monitoring by probabilistic support vector machine

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