采用深度学习的铣刀磨损状态预测模型

doi:10.3969/j.issn.1004-132X.2020.17.009

摘要/Abstract

摘要： 为提高刀具磨损监测的预测精度与泛化性能，研究了基于深度学习的铣刀磨损状态预测，提出了基于堆叠稀疏自动编码网络与卷积神经网络的两种预测模型。堆叠稀疏自动编码网络对特征向量进行降维并将其纳入分类器来实现预测，可避免特征选择对先验知识的依赖；卷积神经网络将铣削振动数据转化为小波尺度图并输入模型完成分类，精简了传统建模流程。最后将提出的两种模型与传统神经网络模型进行比较，验证了所提模型的效率与精度。

关键词: 刀具磨损, 小波变换, 自动编码器, 深度学习

Abstract: In order to improve the prediction accuracy and generalization performance of tool wear monitoring, the milling tool wear state prediction was studied based on deep learning. Two prediction models were proposed based on stacked sparse auto-encoder network and convolutional neural network. The stack sparse auto-encoder network used dimensionality reduction processing of feature vectors and incorporated them into the classifier to achieve classification prediction, avoiding the dependence on prior knowledges in feature selection. Convolutional neural networks completed the conversion of milling vibration data into wavelet scale maps as model inputs, and greatly simplified the traditional modeling processes. Finally, the two proposed models were compared with traditional neural network models to verify the efficiency and accuracy of the proposed models.

Key words: tool wear, wavelet transform, autocoder, deep learning

中图分类号:

TP391
TH164

戴稳1;张超勇1;孟磊磊1;薛燕社1;肖鹏飞1;尹勇2. 采用深度学习的铣刀磨损状态预测模型[J]. 中国机械工程, DOI: 10.3969/j.issn.1004-132X.2020.17.009.

DAI Wen1;ZHANG Chaoyong1;MENG Leilei1;XUE Yanshe1;XIAO Pengfei1;YIN Yong2. Prediction Model of Milling Cutter Wear Status Based on Deep Learning[J]. China Mechanical Engineering, DOI: 10.3969/j.issn.1004-132X.2020.17.009.

参考文献

[1]REHORN A G, JIANG J, ORBAN P E.State-of-the-art Methods and Results in Tool Condition Monitoring: a Review[J].The International Journal of Advanced Manufacturing Technology, 2005, 26(7/8):693-710.
[2]李威霖.车铣刀具磨损状态监测及预测关键技术研究[D].成都：西南交通大学, 2013.
LI Weilin.Research on Key Technologies of Tool Condition Monitoring and Prediction in Turning and Milling[D].Chengdu: Southwest Jiaotong University, 2013.
[3]陶欣，朱锟鹏，高思煜. 基于形态分量分析的高速铣削加工刀具磨损在线监测[J]. 中国科学技术大学学报，2017，47（8）：699-707.
TAO Xin, ZHU Kunpeng, GAO Siyu.On-line Monitoring of Tool Wear in High Speed Milling Based on Morphological Component Analysis[J].Journal of University of Science and Technology of China, 2017, 47(8): 699-707.
[4]曹伟青，傅攀，张尔卿. 遗传算法优化的模糊神经网络在刀具磨损诊断中的应用[J].机械科学与技术，2014，33（11）：1682-1687.
CAO Weiqing, FU Pan, ZHANG Erqing.Application of GA-fuzzy-neural Networks in Tool Wear Diagnosis[J].Mechanical Science and Technology, 2014,33(11):1682-1687.
[5]孙巍伟，黄民，高延. 基于EMD-HMM的机床刀具磨损故障诊断[J]. 机床与液压，2017，45（13）：1682-1687.
SUN Weiwei, HUANG Min, GAO Yan.CNC Tools Wearing Fault Diagnosis Based on EMD-HMM[J].Machine Tool and Hydraulic,2017,45(13):178-181.
[6]林颖，刘亚俊，陈忠.基于分形理论和神经网络的刀具磨损监测[J].中国机械工程，2004, 15(16):20-22.
LIN Ying, LIU Yajun, CHEN Zhong.Tool-wear Detection Using Fractal Theory and Neural Network[J].China Mechanical Engineering, 2004, 15(16):20-22.
[7]曹大理, 孙惠斌, 张纪铎, 等.基于卷积神经网络的刀具磨损在线监测[J/OL].计算机集成制造系统，2018：1-12[2019-03-01].http:kns.cnki.net/kcms/detail/11.5946.TP.20180913.1536.020.html.
CAO Dali, SUN Huibin, ZHANG Jiduo,et al.In-process Tool Condition Monitoring Based on Convolution Neural Network[J/OL].Computer Integrated Manufacturing System，2018: 1-12[2019-03-01].http:kns.cnki.net/kcms/detail/11.5946.TP.20180913.1536.020.html.
[8]张存吉.智慧制造环境下感知数据驱动的加工作业主动调度方法研究[D].广州：华南理工大学，2016.
ZHANG Cunji. Research on Proactive Scheduling Approaches for Job-shops Based on Sensory Data in Wisdom Manufacturing[D].Guangzhou: South China University of Science and Engineering, 2016.
[9]时培明，梁凯，赵娜，等.基于深度学习特征提取和粒子群支持向量机状态识别的齿轮智能故障诊断[J].中国机械工程，2017, 28(9):1056-1061.
SHI Peiming, LIANG Kai, ZHAO Na, et al.Intelligent Fault Diagnosis for Gears Based on Deep Learning Feature Extraction and Particle Swarm Optimization SVM State Identification[J].China Mechanical Engineering, 2017, 28(9):1056-1061.
[10]林杨，高思煜，刘同舜，等.基于深度学习的高速铣削刀具磨损状态预测方法[J].机械与电子, 2017, 35(7): 12-17.
LIN Yang, GAO Siyu, LIU Tongyu, et al.A Deep Learning-based Method for Tool Wear State Prediction in High Speed Milling[J].Machinery & Electronics, 2017, 35(7):12-17.
[11]孙文珺，邵思羽，严如强. 基于稀疏自动编码深度神经网络的感应电动机故障诊断[J].机械工程学报, 2016, 52(9):65-71.
SUN Wenjun, SHAO Siyu, YAN Ruqiang.Induction Motor Fault Diagnosis Based on Deep Neural Network of Sparse Auto-encoder[J].Journal of Mechanical Engineering, 2016, 52(9):65-71.
[12]刘辉海，赵星宇，赵洪山，等.基于深度自编码网络模型的风电机组齿轮箱故障检测[J].电工技术学报, 2017, 32(17):156-163.
LIU Huihai, ZHAO Xingyu, ZHAO Hongshan, et al.Fault Detection of Wind Turbine Gearbox Based on Deep Autoencoder Network[J].Transactions of China Electrotechnical Society, 2017, 32(17):156-163.
[13]KRIZHEVSKY A, SUTSKEVER I, HINTON G.Image Net Classification with Deep Convolutional Neural Networks[J]. Cmmunications of the ACM, 2017, 60(6): 1-9.
[14]PAN S J, YANG Q.A Survey on Transfer Learning[J].IEEE Transactions on Knowledge & Data Engineering, 2010, 22(10):1345-1359.
[15]彭朋.BTA深孔钻削钻头磨损状态卷积神经网络识别技术研究[D].西安：西安理工大学, 2017.
PENG Peng.Research on Convolution Neural Network Identification Technology for Wear State of BTA Deep Drilling Bit [D].Xi'an：Xi'an University of Technology, 2017.
[16]陈泽鑫.小波基函数在故障诊断中的最佳选择[J].机械科学与技术, 2005, 24(2):172-175.
CHEN Zexin.The Selection of Wavelet Base in Malfunction Diagnosis[J].Mechanical Science and Technology, 2005, 24(2):172-175.
[17]HUANG G B, ZHU Q Y, SIEW C K .Extreme Learning Machine: a New Learning Scheme of Feedforward Neural Networks[C]∥2004 IEEE International Joint Conference on Neural Networks.Budapest, 2004: 10.1109/IJCNN.2004.1380068.