结合深度学习的工业大数据应用研究

doi:10.11959/j.issn.2096-0271.2018046

Abstract

Abstract:

How to combine the core technologies such as big data with the smart manufacturing to increase productivity,quality and reduce costs,which is a key task for a new generation of manufacturing innovation.Aiming at the common problem of consumption redundancy to the machine tools in industry,a method based on big data and artificial intelligence to accurately predict the breakage of machine tools was proposed,which achieves an increase in the productively of machine tools and reduces the cost of production.Compared with the previous methods which use the data statistics and traditional machine learning to predict the tool wear,the spindle current value by a high speed collector was got and the strong fitting of convolutional neural network and the strong generalization ability of anomaly detection algorithms was combined.The network could get faster convergence,higher prediction accuracy and robustness.

Key words: deep learning, industrial big data, intelligent manufacturing, anomaly detection, tool breakage

CLC Number:

TP391

Guang LI, Xin YANG. An industrial big data application research using deep learning[J]. Big Data Research, 2018, 4(5): 3-14.

Figures/Tables 11

References 20

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参数	值
机床型号	Super mc f2.0-i/s
刀具类型	端铣刀
刀具材料	硬质合金
刀具规格	701B-DJ 4244-50
工件材料	S136
加工方法	顺铣
机床转速	6 500 r/min
机床进给	1 500 mm/min
纵向进给	2.0
侧向进给	0.1

参数	CNN	BP
网络深度/层	5	3
学习率	0.000 1	0.000 1
激励函数	Relu	Relu
输入大小/bit	4×7 200	4×7 200
样本数/个	20	20
优化方法	Adam	随机下降
总循环次数/次	2 400	2 400

参数	BP	CNN
迭代次数/次	3 600	3 600
样本数/个	20	20
训练集准确率	0.7	1
测试集准确率	0.86	0.964 6

模型	准确率	召回率	F1-Score^[11]
BP	86%	88.68%	86.875%
SVM	92.92%	100%	91.892%
CNN	96.46%	100%	97.87%
CNN-AD	100%	100%	100%

An industrial big data application research using deep learning

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Figures/Tables 11

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开始时间/min	结束时间/min	切削声音
0	17	轻微切削声
17	40	正常切削声
40	52	较大切削声
52	61	尖锐切削声

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