Framework and case study of cognitive maintenance in Industry 4.0

Bao-rui LI; Yi WANG; Guo-hong DAI; Ke-sheng WANG

doi:10.1631/FITEE.1900193

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Framework and case study of cognitive maintenance in Industry 4.0

Special Feature on Industrial Internet | Updated：2022-05-19

- Framework and case study of cognitive maintenance in Industry 4.0
- 工业4.0中认知维护框架与案例研究
- Frontiers of Information Technology & Electronic Engineering Vol. 20, Issue 11, Pages: 1493-1504(2019)
- Affiliations：
  
  Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200444, China
  School of Business, University of Plymouth, Plymouth Devon PL4 8AA, UK
  School of Mechanical Engineering, Changzhou University, Changzhou 213164, China
  Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim N-7491, Norway
- Author bio：
  
  Ke-sheng WANG, E-mail: kesheng.wang@ntnu.no
- Funds：
- DOI：10.1631/FITEE.1900193
  CLC： TP391
- Received：14 April 2019，
  
  Revised：2019-10-14，
  
  Published：2019-11
- Accepted：
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Bao-rui LI, Yi WANG, Guo-hong DAI, et al. Framework and case study of cognitive maintenance in Industry 4.0[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(11): 1493-1504.
DOI：

Bao-rui LI, Yi WANG, Guo-hong DAI, et al. Framework and case study of cognitive maintenance in Industry 4.0[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(11): 1493-1504. DOI： 10.1631/FITEE.1900193.

摘要

提出一种基于信息物理系统和先进人工智能技术的认知维护（CM）框架。CM系统架构集成了智能深度学习方法和智能决策技术，可服务于尖端设备的专业维护人员。该系统将为实时在线维护任务提供技术解决方案，避免因设备故障导致的停机，确保设备和制造资产的持续健康运行。CM实现框架由信息物理系统、物联网、数据挖掘和服务互联网4个模块组成。在数据挖掘模块中，采用深度学习方法实现故障诊断和预测。在实例分析中，以尖端机床侧隙误差为例。利用一个深度置信网络预测机床侧隙误差，预测机床可能发生的故障，实现机床的CM策略。通过案例分析，探讨了在尖端设备上实施CM的意义，并验证了CM实施框架。最后总结了CM系统在制造企业的一些应用经验。

Abstract

We present a new framework for cognitive maintenance (CM) based on cyber-physical systems and advanced artificial intelligence techniques. These CM systems integrate intelligent deep learning approaches and intelligent decision-making techniques

which can be used by maintenance professionals who are working with cutting-edge equipment. The systems will provide technical solutions to real-time online maintenance tasks

avoid outages due to equipment failures

and ensure the continuous and healthy operation of equipment and manufacturing assets. The implementation framework of CM consists of four modules

i.e.

cyber-physical system

Internet of Things

data mining

and Internet of Services. In the data mining module

fault diagnosis and prediction are realized by deep learning methods. In the case study

the backlash error of cutting-edge machine tools is taken as an example. We use a deep belief network to predict the backlash of the machine tool

so as to predict the possible failure of the machine tool

and realize the strategy of CM. Through the case study

we discuss the significance of implementing CM for cutting-edge equipment

and the framework of CM implementation has been verified. Some CM system applications in manufacturing enterprises are summarized.

关键词

Keywords

references

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