Research on Tool Wear Recognition Method based on One-dimensional Residual Convolutional Neural Network
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摘要: 传统的机器学习方法对于刀具磨损进行监测时需要人为提取特征,并且在刀具磨损监测过程出现所需时间较长、精度低等问题。本文提出基于一维残差卷积神经网络的刀具磨损状态识别方法。对原始振动信号进行小波包阈值降噪、快速傅里叶变换处理后,将生成的频谱数据作为残差卷积神经网络模型的输入,通过卷积连接、残差连接和融合等操作自动进行特征提取,最后与刀具磨损状态进行匹配。结果表明:与目前常用的其它神经网络相比较,本文所提出的方法在多次测试中后平均准确率提高了0.6%,训练耗时对于频谱图输入降低30%,具有流程简单、准确率更高的特点,相比于其他方法更有优势。Abstract: The traditional machine learning methods used to tool wear monitoring often need to manually extract features, and require a long time and have low accuracy in the process of tool wear monitoring. In this paper, a tool wear condition recognition method is proposed based on one-dimensional residual convolution neural network. Firstly, the original vibration signal was processed by wavelet packet threshold denoising and fast Fourier transform. Then, the generated spectrum data was taken as a input of the residual convolution neural network model, and the feature was extracted automatically through convolution connection, residual connection and fusion, and finally matched with the tool wear state. The results show that compared with other commonly used neural networks, the proposed method in this paper improves the average accuracy by 0.6% after multiple tests and reduces the training time by 30% for spectrogram input. Thus the proposed method has the characteristics of simple process and higher accuracy, and has more advantages than other methods.
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Key words:
- vibration signal /
- residual connection /
- tool wear /
- convolutional neural network
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表 1 Cr12频谱数据集样本数量
磨损状态 训练集 测试集 初期磨损 273 117 正常磨损 392 168 急剧磨损 301 129 表 2 不同模型测试结果分析
序号 网络模型 迭代次数 特征集数 耗时/s 1 一维卷积网络 3500 512 900 2 本文方法 3500 512 1260 3 Alexnet 3500 128*128 1800 4 Lstm 3500 128 1238 表 3 对比试验准确率
% 类别 1 2 3 4 5 平均准
确率一维卷积网络 99.03 99.28 99.28 99.03 99.28 99.18 Alexnet 99.28 99.28 99.03 99.28 99.28 99.23 Lstm 95.65 96.38 97.10 95.77 96.38 96.256 本文
方法99.76 100 99.52 99.76 100 99.808 表 4 45#钢频谱数据集样本数量
磨损状态 训练集 测试集 初期磨损 414 138 正常磨损 1314 438 急剧磨损 990 330 -
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