结合深度学习的工业大数据应用研究

doi:10.11959/j.issn.2096-0271.2018046

摘要/Abstract

摘要：

如何将大数据等核心技术与智能制造结合，进一步提高产能与质量，并且降低成本，是新一代制造业革新的关键任务。通过一个具体应用案例，即针对工业中常见的机床刀具消耗冗余的问题，提出了基于大数据和人工智能的方法，以准确预测机床刀具的崩刃，从而增加了机床的生产效率，降低了生产成本。相对于以往使用数据统计和传统机器学习进行刀具磨损预测的方法，新方法通过高速电流采集器获取主轴电流值，结合了卷积神经网络的强拟合性和异常检测算法的强泛化能力，对大数据量的电流值进行预测分析，实现了更快的网络收敛及更高的预测准确率和顽健性。

关键词: 深度学习, 工业大数据, 智能制造, 异常检测, 刀具磨损

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

中图分类号:

TP391

李广, 杨欣. 结合深度学习的工业大数据应用研究[J]. 大数据, 2018, 4(5): 3-14.

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

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参考文献 20

[1]	ZHONG R Y , XU X , KLOTZ E ,et al. Intelligent manufacturing in the context of industry 4.0:a review[J]. Engineering, 2017,3(5): 616-630.
[2]	SHEN W , NORRIE D H . Agent-based systems for intelligent manufacturing:a state-of-the-art survey[J]. Knowledge ＆Information Systems, 1999,1(2): 129-156.
[3]	WIENDAHL H P , SSHOLTISSEK P . Management and control of complexity in manufacturing[J]. CIRP Annals Manufacturing Technology, 1994,43(2): 533-540.
[4]	李杰 . 从大数据到智能制造[M]. 倪军, 王安正,译. 上海: 上海交通大学出版社, 2016.
	LI J . From big data to intelligent manufacturing[M]. Translated by NI J,WANG A Z. Shanghai: Shanghai Jiao Tong University PressPress, 2016.
[5]	LECUN Y , BENGIO Y , HINTON G . Deep learning[J]. Nature, 2015,521(7553):436
[6]	TAN Y P , ACHARYA T . A robust sequential approach for the detection of defective pixels in an image sensor[C]// 1999 IEEE International Conference on Acoustics,Speech,and Signal Processing,March 15-19,1999,Phoenix,USA. Piscataway:IEEE Press, 1999.
[7]	NIE P , WANG D L , XU T ,et al. Application of frequency band energy character method in cutter wear monitoring system using acoustic emission[J]. Tool Engineering, 2009.
[8]	GHANI A K , CHOUDHURY I A ， HUSNI . Study of tool life,surface roughness and vibration in machining nodular cast iron with ceramic tool[J]. Journal of Materials Processing Tech, 2002,127(1): 17-22.
[9]	SNR D E D . Sensor signals for tool-wear monitoring in metal cutting operations:a review of methods[J]. International Journal of Machine Tools ＆ Manufacture, 2000,40(8): 1073-1098.
[10]	CHANDOLA V , BANERJEE A , KUMAR V . Anomaly detection:a survey[J]. ACM Computing Surveys, 2009,41(3): 1-58.
[11]	JOSIPA C . Introduction to modern information retrieval[J]. McGraw-Hill, 1983,55(4): 239-240.
[12]	GOODFELLOW I J , POUGET-ABADIE J , MIRZA M ，et al. Generative adversarial nets[C]// International Conference on Neural Information Processing Systems,November 3-6,2014,Kuching,Malaysia. Massachusetts:MIT Press, 2014: 2672-2680.
[13]	MIRZA M , OSINDERO S . Conditional generative adversarial nets[J]. Computer Science, 2014: 2672-2680.
[14]	CHEN X , DUAN Y , HOUTHOOFT R ,et al. InfoGAN:interpretable representation learning by information maximizing generative adversarial nets[J]. Conference and Workshop on Neural Information Processing Systems, 2016: 2172-2180.
[15]	RADFORD A , METZ L , CHINTALA S . Unsupervised representation learning with deep convolutional generative adversarial networks[J]. Computer Science, 2015，arXiv:1511.06434.
[16]	ARJOVSKY M , CHINTALA S , BOTTOU L . Wasserstein GAN[J]. Statistics, 2015,arXiv:1701.07875.
[17]	PAN S J , YANG Q . A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010,22(10): 1345-1359.
[18]	WEISS K , KHOSHGOFTAAR T M , WANG D D . A survey of transfer learning[J]. Journal of Big Data, 2016,3(1):9.
[19]	III H D , . Frustratingly easy domain adaptation[C]// The 45th Annual Meeting of the Association for Computational Linguistics,June 28-30,2007,Prague,Czech Republic.[S.l.:s.n]. 2007: 256-263.
[20]	ZHU Y , CHEN Y , LU Z ,et al. Heterogeneous transfer learning for image classification[C]// 2011 AAAIConference on Artificial Intelligence,August 7-11,2011,San Francisco,USA. Palo Alto:AAAI Press, 2011: 1304-1309.

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%