改进VMD-LSTM法在刀具磨损状态识别中的应用

Application of Modified VMD and LSTM in Tool Wear State Recognition Model

针对车刀在实际加工时工况复杂导致磨损状态识别精度不高的问题,提出了一种基于最大包络峰度法的变分模态分解(Variational mode decomposition,VMD)结合长短时记忆网络(Long short-term memory,LSTM)的组合分类算法。采用最大包络峰度法确定VMD最佳分解模态数,计算信噪比对高频信号进行降噪重构,然后对原始信号以及分解后的信号进行特征提取和清洗,针对数据样本不均衡的问题,引入SMOTE算法合成少数类样本,结合特征变化以及刀具加工过程中的磨损划分数据集,使用LSTM模型实现多工况下车刀磨损状态的分类。最后通过实验验证所提出的模型和方法的有效性,实验结果表明,此模型与其他分类模型相比具有更高的分类精度以及更好的泛化性。

Aiming at the problem of low accuracy of wear state recognition due to complex working conditions of turning tools in actual machining,a combined classification algorithm of variational mode decomposition(VMD)based on the maximum envelope kurtosis combined with long and short-term memory(LSTM)networks is proposed in this paper.First,the maximum envelope kurtosis method is used to determine the optimal decomposition mode number of VMD,the signal-to-noise ratio is calculated to reduce the noise and reconstruct the high-frequency signal,and then the original signal and the decomposed signal are feature extraction and cleaning.Then,aiming at the unbalanced data sample for the problem,the SMOTE algorithm is introduced to synthesize a minority of samples,combined with feature changes and the wear division data set during tool processing,and the LSTM model is used to classify the wear status of the turning tool under multiple working conditions.Finally,the effectiveness of the proposed model and method is verified through experiments.The experimental results show that this model has higher classification accuracy and better generalization than other classification models.