OPTIMAL ASSEMBLY OF SUPPORT VECTOR REGRESSORS WITH APPLICATION TO SYSTEM MONITORING

Abstract

Power plants are high complexity systems running risks of low frequency but high consequence. The field of machine learning appears to offer the necessary tools for developing automated instrument surveillance systems supporting decision-making in critical systems such as power stations. A novel prediction method is presented with the aim to enhance system safety and performance by making an ahead-of-time prediction of the status of fundamental system components and subsequent detection of abnormalities. The utilization of a linear assembly of support vector regressors employing unique kernels is proposed in a hybrid computational scheme that encompasses the formulation of a multi-objective optimization problem addressed with an evolutionary algorithm that employs Pareto theory to identify an optimal solution. The approach is tested on the ahead of time prediction of the crack length in power plant turbine blades utilizing historical data. The results obtained highlight the efficiency of the proposed methodology since better performance over the standalone support vector regressors is observed.