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

作者：Liu Jie^*; Vitelli Valeria; Zio Enrico; Seraoui Redouane

来源：IEEE Transactions on Reliability, 2015, 64(4): 1203-1213.

DOI：10.1109/TR.2015.2427156

• Ensemble
• nuclear Pressurized Water Reactor
• probabilistic support vector regression
• prognostics
• Reactor Coolant Pump
• time series
• uncertainty quantification

In this paper, a novel Dynamic-Weighted Probabilistic Support Vector Regression-based Ensemble (DW-PSV-Rensemble) approach is proposed for prognostics of time series data monitored on components of complex power systems. The novelty of the proposed approach consists in i) the introduction of a signal reconstruction and grouping technique suited for time series data, ii) the use of a modified Radial Basis RBF) kernel for multiple time series data sets, iii) a dynamic calculation of sub-models weights for the ensemble, and iv) an aggregation method for uncertainty estimation. The dynamic weighting is introduced in the calculation of the sub-models' weights for each input vector, based on Fuzzy Similarity Analysis (FSA). We consider a real case study involving 20 failure scenarios of a component of the Reactor Coolant Pump (RCP) of a typical nuclear Pressurized Water Reactor (PWR). Prediction results are given with the associated uncertainty quantification, under the assumption of a Gaussian distribution for the predicted value.

• 出版日期2015-12