Electromechanical equipment state forecasting based on genetic algorithm – support vector regression

Prediction of electromechanical equipments state nonlinear and non-stationary condition effectively is significant to forecast the lifetime of electromechanical equipments. In order to forecast electromechanical equipments state exactly, support vector regression optimized by genetic algorithm is proposed to forecast electromechanical equipments state. In the model, genetic algorithm is employed to choose the training parameters of support vector machine, and the SVR forecasting model of electromechanical equipments state with good forecasting ability is obtained. The proposed forecasting model is applied to the state forecasting for industrial smokes and gas turbine. The experimental results demonstrate that the proposed GA-SVR model provides better prediction capability. Therefore, the method is considered as a promising alternative method for forecasting electromechanical equipments state.

Research highlights
► Electromechanical equipment state forecasting model is established by using genetic algorithm-support vector regression.
► Genetic algorithm is employed to choose the training parameters of support vector regression.
► Leave-one-out cross-validation(LOOCV) is adopted to evaluate the fitness of the training parameters of support vector regression.