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

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

Dynamic Weighted PSVR-Based Ensembles for Prognostics of Nuclear Components

International audienceCombining different physical and / or statistical predictive algorithms for Nuclear Power Plant (NPP) components into an ensemble can improve the robustness and accuracy of the prediction. In this paper, an ensemble approach is proposed for prediction of time series data based on a modified Probabilistic Support Vector Regression (PSVR) algorithm. We propose a modified Radial Basis Function (RBF) as kernel function to tackle time series data and two strategies to build diverse sub-models of the ensemble. A simple but effective strategy is used to combine the results from sub-models built with PSVR, giving the ensemble prediction results. A real case study on a power production component is presented

A Novel Dynamic-Weighted Probabilistic Support Vector Regression-Based Ensemble for Prognostics of Time Series Data

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Numerical methods for applying onshore failure rate to offshore operational conditions and assessing the benefits of condition monitoring

The cost effectiveness of condition monitoring systems for offshore wind energy is uncertain and thus a research concern. Unlike onshore wind turbines, the O&M costs of offshore wind turbines are directly affected by the marine environment, mainly wind and wave conditions, and vessel costs are a main contributor to offshore O&M costs. The lack of offshore O&M data makes wind turbine component failure rates essential for component risk assessment and cost model design. However, the failure rate data, especially from the offshore turbines, are highly protected by the manufacturers and the offshore operators. The method developed in this paper deals with this lack of data by adjusting onshore failure rate data to offshore to account for the different operational conditions. Internal cooperation of a large onshore wind farm in the UK and a large Swedish offshore wind farm has been reached, and three-year-period operational data records are used for this research. Both the sites use Siemens -2.3-93 turbines, which enable the comparison with minimum deviation caused by the types of the turbines between onshore and offshore.Failure Modes Effect and Criticality Analysis (FMECA), which follows the U.S. Military Standard 1629a, has been applied in many industrial areas including onshore wind energy successfully but as yet there are no examples in an offshore wind context. In most cases, the components were ranked using the Risk Priority Number, and in one instance, a cost priority number.A statistical cost model with specific concern for offshore vessel cost and marine access thresholds is presented in this paper and compared with other cost models in the research domain. A sensitivity analysis is applied to the selected offshore wind farm through the cost model in terms of changing the failure detectability of the condition monitoring system, and compares the effectiveness of maintenance with and without condition monitoring

Nuclear power plant components condition monitoring by probabilistic support vector machine

International audienceIn 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