摘要基于变量预测模型的模式识别(Variable predictive model based class discriminate,简称VPMCD)方法在训练过程中是用多项式响应面(Polynomial Response Surface,简称PRS)法来建立预测模型的,然而PRS法的模型拟合精度不能随训练样本容量的增加而显著提高。针对这一缺陷,将原方法中的PRS方法进行了改进,提出了基于改进多项式响应面(Improved Polynomial Response Surface,简称IPRS)法的VPMCD方法,并将其应用于滚动轴承故障诊断。通过实验,将原方法和改进方法在训练样本容量不同情况下的模式分类精度进行对比,结果表明,相对于原VPMCD方法,改进的VPMCD方法不仅具有更好的模式分类效果,而且其分类精度随训练样本容量的增加提高得更明显。
The training process of Variable predictive model based class discriminate (VPMCD) approach is to establish the predictive model with Polynomial Response Surface (PRS) method. However, the model fitting precision of PRS can't significantly improve with the increase of the capacity of training sample. According to this defect, the PRS method is improved in this paper and the method of VPMCD based on Improved Polynomial Response Surface (IPRS) is proposed, and then applied to rolling bearing fault diagnosis. Through the experiment, the pattern classification accuracy of the original method and the improved method are compared under the condition of different training sample sizes. The results show that compared with the original VPMCD method, improved VPMCD method not only has better effect of pattern classification, but also its classification precision enhances more obviously with the increase of the training sample size.
杨宇;潘海洋;李杰;程军圣. 基于改进多项式响应面的VPMCD方法及其在滚动轴承故障诊断中的应用[J]. , 2014, 33(19): 157-163.
YNAG Yu PAN Hai-yang LI Jie CHENG Jun-sheng. The VPMCD approach based on the improved polynomial response surface and its application in the rolling bearing fault diagnosis. , 2014, 33(19): 157-163.