Machine learning applications in Cyber-Physical Production Systems: a survey

Zili Zhang, Chao Liu, Jun Zhang, Tao Peng, Xinrong Hu, Yuchun Xu

Research output: Chapter in Book/Published conference outputConference publication

Abstract

Cyber-Physical Production Systems (CPPS) play a vital role in realizing the vision of Industry 4.0. In the last decade, various machine learning methods have been implemented in manufacturing systems to improve their intelligence. However, few review papers on machine learning applications in CPPS have been published. In this context, this paper presents a survey of machine learning applications in Cyber-Physical Production Systems. Both bibliometric analysis and qualitative analysis have been conducted based on the related literatures published in the last decade. We identified the major research issues with respect to machine learning applications in CPPS, i.e. anomaly detection, predictive maintenance, fault management, efficiency, quality assurance, and scheduling. The review results show that although machine learning has been extensively applied in manufacturing, its applications in CPPS have not been widely studied. Based on the detailed discussions of the research issues and challenges, this paper indicates the current limitations of CPPS and demonstrates the great advantages and potential for applying machine learning in CPPS in future research.
Original languageEnglish
Title of host publication2022 27th International Conference on Automation and Computing (ICAC)
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-6654-9807-4
ISBN (Print)978-1-6654-9808-1
DOIs
Publication statusPublished - 10 Oct 2022
Event2022 27th International Conference on Automation and Computing (ICAC) - Bristol, United Kingdom
Duration: 1 Sep 20223 Sep 2022

Conference

Conference2022 27th International Conference on Automation and Computing (ICAC)
Period1/09/223/09/22

Keywords

  • Cyber-Physical Production System
  • anomaly detection
  • machine learning
  • predictive maintenance
  • review
  • scheduling

Fingerprint

Dive into the research topics of 'Machine learning applications in Cyber-Physical Production Systems: a survey'. Together they form a unique fingerprint.

Cite this