离散隐马尔可夫模型在颤振预报中的应用研究

对于切削过程中颤振孕育的动态模式,提出了基于离散隐马尔可夫模型(DHMM)的模式识别理论预报颤振的新方法。首先对切削过程的振动信号进行FFT特征提取,然后利用自组织特征映射(SOM)神经网络对提取的特征矢量进行冗余信息压缩与预分类编码;再根据多变量DHMM建模理论,对切削颤振孕育的各种过程模式建立相应的DHMM,把矢量编码作为观测序列引入到DHMM中进行机器学习、训练;最后将观测序列引入到DHMM中进行颤振孕育的概率识别尝试。实验表明,该方法对颤振孕育过程识别是十分有效的,颤振预报正确率达93.3%。

Application of DHMM Pattern Recognition Theory to Chatter Prediction

A new method of chatter prediction based on discrete hidden Markov models(DHMM) is proposed for dynamic patterns of chatter gestation in cutting process.At first,FFT(fast Fourier transtorm) features are extracted from the vibration signal of cutting process,then FFT vectors are presorted and coded into code books of integer numbers by self-organizing maps(SOM),and these code books are introduced to DHMM for machine learning and classification.Finally the results of chatter gestation recognition and chatter prediction experiments are presented,and the results show that the method proposed is effective,and recognition rate of chatter gestation is 93.33%.