An industrial big data application research using deep learning

Guang LI; Xin YANG

doi:10.11959/j.issn.2096-0271.2018046

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An industrial big data application research using deep learning

TOPIC：PRACTICAL INNOVATIONS OF BIG DATA | 更新时间：2024-06-03

- An industrial big data application research using deep learning
- Big Data Research Vol. 4, Issue 5, Pages: 2018046(2018)
- 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：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2018046
  CLC： TP391
- Online First：2018-09，
  
  Published：15 September 2018
- 稿件说明：
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Guang LI, Xin YANG. An industrial big data application research using deep learning[J]. Big Data Research, 2018, 4(5): 2018046.
DOI：

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.

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Keywords

references

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