结合深度学习的工业大数据应用研究

李广; ，杨欣

doi:10.11959/j.issn.2096-0271.2018046

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结合深度学习的工业大数据应用研究

专题：大数据创新实践 | 更新时间：2024-06-03

- 结合深度学习的工业大数据应用研究
- An industrial big data application research using deep learning
- 大数据 2018年4卷第5期页码：2018046
- 作者机构：
  
  1. 电子科技大学大数据研究中心，四川成都 611731
  2. 成都数之联科技有限公司，四川成都 610041
- 作者简介：
  
  [ "李广（1996-），男，电子科技大学大数据研究中心硕士生，主要研究方向为机器学习与深度学习。2018年获IEEE BCD2018会议最佳学生论文奖。" ]
  [ "杨欣（1983-），男，成都数之联科技有限公司研发总监，主要研究方向为人工智能、数据挖掘、物联网等。发表SCI论文和CCF-A会议论文10余篇，2018年获日本情报处理学会卓越研究奖。" ]
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2018046
  中图分类号： TP391
- 网络首发：2018-09，
  
  纸质出版：2018-09-15
- 稿件说明：
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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):2018046.

Guang LI, Xin YANG. An industrial big data application research using deep learning[J]. Big Data Research, 2018, 4(5): 2018046.
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):2018046. DOI： 10.11959/j.issn.2096-0271.2018046.

Guang LI, Xin YANG. An industrial big data application research using deep learning[J]. Big Data Research, 2018, 4(5): 2018046. DOI： 10.11959/j.issn.2096-0271.2018046.

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。

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.

关键词

Keywords

references

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