中文摘要：

提出了一种基于时序AR模型的VPMCD（基于变量预测模型的模式识别）故障诊断方法：利用时序分析方法对故障信号建立AR模型，以蕴含故障特征的自回归参数作为故障特征量，采用VPM-CD方法训练得到各故障特征量的预测模型，并利用预测模型对待诊断样本的故障类型和工作状态进行分类和识别。对滚动轴承和齿轮的振动信号的分析结果证明了该方法的有效性，与基于EMD的VPMCD法和基于AR的KNN法的对比结果证明了所提方法的优越性。

英文摘要：

A pattern recognition method was proposed herein based on fusion of time series analy- sis AR model with VPMCD for fault diagnosis. AR model of fault signals was established by using time series analysis, taking its autoregressive parameters that contain the fault features as the fault characteristic values,fusing VPMCD training to get the prediction models of fault characteristic val- ues,and by using these predictive models to classify and recognize the faults of sample types and working states. Analyses of roiling bearings and gear vibration signals show the effectiveness of this method,comparison of the diagnosis method based on fusion of empirical mode decomposition（EMD） with VPMCD shows the superiority of .this method.