基于全矢最小二乘支持向量机的设备状态趋势预测

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

在支持向量机(SVM)基础上拓展出的最小二乘支持向量机(LS-SVM)非线性泛化能力更好,具有较高的拟合和预测精度,目前被广泛应用于设备状态趋势预测中。为进一步提高其预测精度,结合基于同源信息融合的全矢谱技术提出一种新的趋势预测方法——全矢LS-SVM。该方法采用全矢谱技术融合双通道信息,相比传统单通道信号提取方法,保障LS-SVM预测数据特征提取的完整性,提高预测精度。将该方法应用于某电厂1号汽轮机振动数据的预测,实验结果表明,全矢LS-SVM方法具有较高的预测精度。

Least squares support vector machine（ LS-SVM） developed based on support vector machines（ SVM） with better nonlinear 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.