SIGNAL GROUPING FOR CONDITION MONITORING OF NUCLEAR POWER PLANT COMPONENTS

International audienceThe present work investigates the possibility of building a condition monitoring model by splitting the usually very large number of signals measured by the sensors into subgroups and building a specialized model for each subgroup. Different criteria are considered for selecting the signal groups, such as the location of the measurements (i.e., signals measured in the same area of the plant belong to the same group) and their correlation (i.e., correlated signals are grouped together). A real case study concerning 48 signals selected between those used to monitor the reactor coolant pump of a Pressurized Water Reactor (PWR) is considered in order to verify the monitoring performance of different grouping criteria. Performance metrics measuring accuracy, robustness and spill-over effect have been considered in the evaluation. Key Words: Condition Monitoring, Empirical Modeling, Power Plants, Safety Critical Nuclear Instrumentation, Autoassociative models

Probabilistic Support Vector Regression for Short-Term Prediction of Power Plants Equipment

International audienceA short-term forecasting approach is proposed for the purposes of condition monitoring. The proposed approach builds on the Probabilistic Support Vector Regression (PSVR) method. The tuning of the PSVR hyerparameters, the model identification and the uncertainty analysis are conducted via novel and innovative strategies. A case study is shown, regarding the prediction of a drifting process parameter of a Nuclear Power Plant (NPP) component

AN EFFICIENT ONLINE LEARNING APPROACH FOR SUPPORT VECTOR REGRESSION

International audienceIn this paper, an efficient online learning approach is proposed for Support Vector Regression (SVR) by combining Feature Vector Selection (FVS) and incremental learning. FVS is used to reduce the size of the training data set and serves as model update criterion. Incremental learning can "adiabatically" add a new Feature Vector (FV) in the model, while retaining the Kuhn-Tucker conditions. The proposed approach can be applied for both online training & learning and offline training & online learning. The results on a real case study concerning data for anomaly prediction in a component of a power generation system show the satisfactory performance and efficiency of this learning paradigm

A dynamic weighted RBF-based ensemble for prediction of time series data from nuclear components

International audienceIn this paper, an ensemble approach is proposed for prediction of time series data based on a Support Vector Regression (SVR) algorithm with RBF loss function. We propose a strategy to build diverse sub-models of the ensemble based on the Feature Vector Selection (FVS) method of Baudat & Anouar (2003), which decreases the computational burden and keeps the generalization performance of the model. A simple but effective strategy is used to calculate the weights of each data point for different sub-models built with RBF-SVR. A real case study on a power production component is presented. Comparisons with results given by the best single SVR model and a fixed-weights ensemble prove the robustness and accuracy of the proposed ensemble approach

Genetic Algorithm-based Wrapper Approach for Grouping Condition Monitoring Signal of Nuclear Power Plant Components

Equipment condition monitoring of nuclear power plants requires to optimally group the usually very large number of signals and to develop for each identified group a separate condition monitoring model. In this paper we propose an approach to optimally group the signals. We use a Genetic Algorithm (GA) for the optimization of the groups; the decision variables of the optimization problem relate to the composition of the groups (i.e., which signals they contain) and the objective function (fitness) driving the search for the optimal grouping is constructed in terms of quantitative indicators of the performances of the condition monitoring models themselves: in this sense, the GA search engine is a wrapper around the condition monitoring models. A real case study is considered, concerning the condition monitoring of the Reactor Coolant Pump (RCP) of a Pressurized Water Reactor (PWR). The optimization results are evaluated with respect to the accuracy and robustness of the monitored signals estimates. The condition monitoring models built on the groups found by the proposed approach outperform the model which uses all available signals, whereas they perform similarly to the models built on groups based on signal correlation. However, these latter do not guarantee the robustness of the reconstruction in case of abnormal conditions and require to a priori fix characteristics of the groups, such as the desired minimum correlation value in a group

A novel ensemble clustering for operational transients classification with application to a nuclear power plant turbine

International audienceThe objective of the present work is to develop a novel approach for combining in an ensemble multiple base clusterings of operational transients of industrial equipment, when the number of clusters in the final consensus clustering is unknown. A measure of pairwise similarity is used to quantify the co-association matrix that describes the similarity among the different base clusterings. Then, a Spectral Clustering technique of literature, embedding the unsupervised K-Means algorithm, is applied to the co-association matrix for finding the optimum number of clusters of the final consensus clustering, based on Silhouette validity index calculation. The proposed approach is developed with reference to an artificial case study, properly designed to mimic the signal trend behavior of a Nuclear Power Plant (NPP) turbine during shutdown. The results of the artificial case have been compared with those achieved by a state-of-art approach, known as Cluster-based Similarity Partitioning and Serial Graph Partitioning and Fill-reducing Matrix Ordering Algorithms (CSPA-METIS). The comparison shows that the proposed approach is able to identify a final consensus clustering that classifies the transients with better accuracy and robustness compared to the CSPA-METIS approach. The approach is, then, validated on an industrial case concerning 149 shutdown transients of a NPP turbine

RJMCMC point process sampler for single sensor source separation : an application to electric load monitoring

This paper presents an original method to separate the residential electric load into its major components. The method is explained in the particular case of space-heating, which is the most consuming electric end-use in France1. This is a source separation problem from a single mixture. The components to be retrieved are square signals characterized by a periodic regulation and a slowly timevarying duty cycles. A point process is used to model the electric load as a configuration of possibly overlapping square signals, given the priors on magnitude, duty cycle variations and the regulation periodicity. This stochastic process is simulated using a Reversible Jump Markov Chain Monte Carlo procedure. A simulated annealing scheme is used to achieve the posterior density maximization. First results on real data provided by Electricité de France are quite encouraging

Fault Detection in Nuclear Power Plants Components by a Combination of Statistical Methods

International audienceIn this paper, we investigate the feasibility of a strategy of fault detection capable of controlling misclassification probabilities, i.e., balancing false and missed alarms. The novelty of the proposed strategy consists of i) a signal grouping technique and signal reconstruction modeling technique (one model for each subgroup), and ii) a statistical method for defining the fault alarm level. We consider a real case study concerning 46 signals of the Reactor Coolant Pump (RCP) of a typical Pressurized Water Reactor (PWR). In the application, the reconstructions are provided by a set of Auto-Associative Kernel Regression (AAKR) models, whose input signals have been selected by a hybrid approach based on Correlation Analysis (CA) and Genetic Algorithm (GA) for the identification of the groups. Sequential Probability Ratio Test (SPRT) is used to define the alarm level for a given expected classification performance. A practical guideline is provided for optimally setting the SPRT parameters' values

A numerical method to transfer an onshore wind turbine FMEA to offshore operational conditions

Failure Modes Effect Analysis (FMEA), or more specifically, Failure Modes Effect and Criticality Analysis (FMECA) has been accepted as an effective condition monitoring assessment tool used widely by the mili-tary, traditional industries and reliability relevant engineering systems. A successful FMEA assists to identity, evaluate and report component failure modes, their severity and impact on the systems. FMEA has been al-ready applied to onshore wind turbines, but there is a lack of offshore wind turbine applications. FMEA can be quantified by using the metric of Risk Priority Number (RPN), defined as the product of the levels of event severity, occurrence frequency and detectability. This paper presents an approach that allows the application of RPN to offshore wind energy by identifying correction factors to existing onshore RPN values taken from previous research. This approach estimates offshore failure rates for key wind turbine components from onshore data