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中文摘要：
针对旋转机械振动信号的特征量有时存在非平稳、非线性发展趋势的特点，构建了分整差分函数系数自回归（DFAR）模型。DFAR模型利用函数系数自回归模型建立非参数预测模型，并且根据改进的交叉核实评价准则自动选择分数阶差分或整数阶差分来处理原始数据，估计最优的建模参数。函数系数自回归模型能使得模型参数随模型依赖变量的值连续变化而逐渐变化；分数阶差分能提取时间序列中的确定性趋势信息，同时能防止因过差分而丢失长记忆低频成分，因而DFAR模型能更好地逼近非线性时间序列。实例验证表明，DFAR模型提高了对非平稳、非线性发展的故障特征量的趋势预测精度。
英文摘要：
Aiming at the feature values of vibration signals of rotary machines, which sometimes have non--stationary and nonlinear development characteristics,DFAR （differential functional-coefficient autoregressive） model was presented. DFAR established nonparametric prediction model by using functional--coefficient autoregressive model. It automatically selected fractional or integral difference to process the original data and estimated the optimal model parameters according to the improved cross--validation criterion. The functional--coefficient autoregressive model can make the model pa rameters change with the model dependence variable. The fractional difference can extract the stable trend information from time series and it won＇t lose low frequency because it has no defect of over- difference. So,DFAR can approximate the nonlinear time series better. As shown in the experiments, DFAR can improve the precision of predicting development trend of the non--stationary and nonlinear fault feature values.
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