基于EMD-HMM的机床刀具磨损故障诊断

摘要

针对机床刀具磨损故障诊断,提出基于经验模态分解(EMD)进行信号处理和基于隐马尔科夫模型(HMM)进行模式识别的刀具故障诊断方法.在信号处理阶段,对机加工过程中的振动信号进行经验模态分解,得到若干固有模态函数(IMF),计算IMF的能量值并选用前几阶高能量的IMF作为识别参数.在模式识别阶段,先将训练样本使用HMM的基本方法进行模型训练获得HMMs,再使用测试样本进行模型准确性验证.完成验证的模型就可以表示该机床刀具磨损和机加工刀具信号的对应关系,可以应用到刀具磨损的监测识别中.%For tool wear fault diagnosis,a CNC tools wearing fault diagnosis method was proposed based on empirical mode decomposition(EMD) for signal processing and hidden Markov model(HMM) for pattern recognition.In signal processing stage,empirical mode decomposition was done to vibration signal in the machining process,and then a number of intrinsic mode functions (IMF) were gotten,the energy values of the IMFs were calculated and the first few high-energy stage IMFs were chosen as identification parameters.In pattern recognition stage,HMMs were gotten by training the samples with basic HMM training methods,then the test samples were used to verify the accuracy of the model.After testing,the model can describe the correspondence between the machine tool status and vibration signal of machining tools,which can be applied to the monitoring and identification of tool wear.