An energy-aware cyber physical system for energy Big data analysis and recessive production anomalies detection in discrete manufacturing workshops

Chaoyang Zhang, Zhengxu Wang, Kai Ding, Felix T.S. Chan, Weixi Ji

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)

Abstract

With the development of sensing and communications technology, some new features have emerged in manufacturing processes, such as highly correlated, deeply integrated, dynamically integrated, and a huge volume of data. There is a strong need to deeply excavate information from manufacturing Big Data, especially the energy consumption data, for energy-efficient manufacturing operations management and analysis. However, relevant data reduction and association analysis to support energy-efficient manufacturing are still ineffective and error-prone, especially for discrete manufacturing workshops. In this paper, an energy-aware Cyber Physical System (E-CPS) is proposed for energy Big Data analysis and recessive production anomalies detection. Firstly, E-CPS is introduced to acquire manufacturing Big Data. Then, a Big Data analysis method, including data reduction and data association analysis, is proposed to analyse the manufacturing data in the E-CPS. Considering the complexity and dynamics of manufacturing processes, an energy Big Data-driven recessive production anomalies analysis method is proposed based on deep belief networks. The proposed method in this paper realises the integrated utilisation of production Big Data and energy Big Data in the E-CPS. Further, the efficiency evaluation and recessive anomalies detection methods can be used in existing production information systems.

Original languageEnglish
Pages (from-to)7059-7077
Number of pages19
JournalInternational Journal of Production Research
Volume58
Issue number23
DOIs
Publication statusPublished - Dec 2020

Keywords

  • big data analysis
  • cyber physical system
  • discrete manufacturing
  • energy consumption
  • production anomaly analysis

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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