摘要
针对样本数据量较小条件下的故障预测问题,提出了一种灰色相关向量机(relevance vector ma-chine,RVM)故障预测模型。在模型的训练阶段,根据特征数据序列建立其离散灰色模型(discrete grey model,DGM),以DGM的预测值作为输入、原始数据序列作为输出,训练得到RVM回归预测模型;在模型的预测阶段,由建立的DGM和RVM回归预测模型组合得到灰色RVM故障预测模型,并通过引入新陈代谢过程,不断更新数据中的信息。实验结果表明,模型的预测性能优于传统的灰色预测模型。
To solve the fault prognostic problem caused by small samples, a model hased on grey relevance vector machine （RVM） is presented. At the training stage, the discrete grey model （DGM） is established according to the characteristic data sequence, and the model based on RVM regression is trained by using the forecasting values of DGM as input and using the original data sequence as output; At the forecasting stage, a grey RVM model is established by combining DGM and model based on RVM regression, and the information contained in the data are updated through metabolism. The experiment results show that the model has a better performance than conventional grey models.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2012年第2期424-428,共5页
Systems Engineering and Electronics
关键词
故障预测
灰色模型
相关向量机
新陈代谢
fault prognostic
grey model
relevance vector machine （RVM）
metabolism
