摘要
分析了动力装置运行状态特点和预测要求,依据分形和支持向量回归理论,建立了基于分形与支持向量回归的状态趋势预测模型。其中,以振动烈度作为描述机组状态的特征数据来构建时间序列,对其进行相空间重构,根据最小嵌入维数来确定支持向量机输入节点数,采用支持向量回归算法对机组状态趋势进行预测。应用案例研究和实验对比分析的结果表明,研究的状态预测模型单步预测的平均相对误差为1.7881%,30步预测的平均相对误差为3.3983%,预测模型能较好地满足动力装置状态趋势预测要求。
The operational condition characteristics and the forecasting requirements for power devices were analyzed. A condition forecasting model based on the fractal theory and support vector regression method was presented. As a condition feature, vibration intensity was adopted to construct a time series. Minimum embedding dimension of the phase space was calculated by using phase space restructure technique, which was used to decide the input nodes of the support vector regression model. The forecasting ability and the validity of the model were studied and validated by means of application case and contrastive experiments. The research results demonstrate that relative mean error （RME） of the forecasting model is only 1. 7881% for one step forecasting and 3. 3983% for thirty steps forecasting; and the model can better meet the requirements of the condition forecasting for power devices.
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2008年第1期22-25,共4页
China Mechanical Engineering
关键词
动力装置
分形
支持向量回归
时间序列
预测
power device
fractal
support vector regression
time series
forecasting
