中文摘要：

在机床的连续使用过程中，其相对动柔度第一阶固有频率、最大相对动柔度、平均相对动柔度和品质系数COM均会发生不同程度的改变，因此，对机床相对动柔度劣化趋势进行预测研究就具有非常重要的意义。然而，由于机床相对激振实验会影响机床的正常使用，很难获取大量的机床相对动柔度数据。为解决实验数据的小样本问题，采用广义隐马尔科夫模型（ Generalized Hidden Markov Model， GHMM）以及重心法进行机床相对动柔度劣化趋势研究。结果表明：和隐马尔科夫模型（ Hidden Markov Model， HMM）相比， GHMM具有更高的预测精度，可以很好地解决小样本问题；各个评价指标的准确预测，可以大大减少复杂的相对激振实验次数，快速地得到机床相对动柔度劣化趋势。

英文摘要：

In the continuous cutting processes of a machine tool, the lowest natural frequency,maximum dynamic compliance , average dynamic compliance and Coefficient of Merit of the machine tool would be changed at different degree.Therefore, the research on deterioration trend prediction of the dynamic compliance of a machine tool is very important .However, the relative excitation experi-ment has much influence on the normal usage of a machine tool, it is difficult to obtain a great deal of experimental data.To solve the problem of small sample size of experiment data, Generalized Hidden Markov Model （ GHMM） and gravity method was used to predict the deterioration trend of the dynamic compliance of a machine tool.The research shows that, compared with Hidden Markov Model （ HMM） , GHMM can deal with the problem of small sample size well.Meanwhile, the precise prediction of all evaluation criterions can decrease the number of relative exciting experiments, and can help to obtain the deterioration trend of the dynamic compliance of a machine tool.