Equipment Condition Trend Prediction Based on Full Vector Least Squares Support Vector Machine

Least squares support vector machine ( LS-SVM) developed based on support vector machines ( SVM) with better non-linear generalizationability, has higher fitting and prediction precision. Now it is widely used in equipment condition trend prediction. In order to further improve its prediction accuracy, a new trend prediction method combined with full vector spectrum technology based on information fusion homologous with a same source was proposed—full vector LS-SVM. This method was used of full vector spectrum technology to fuse dual-channel information to ensure integrity of LS-SVM prediction data feature extraction compared to the traditional single-channel signal extraction methods, which improved prediction accuracy. The method is applied to predict the vibration data of No. 1 steam turbinein in a power plant, and the experimental results show that full vector LS-SVM has higher prediction accuracy.