Building a rigorous foundation for performance assurance assessment techniques for 'smart' manufacturing systems

Utpal Roy, Yunpeng Li, Bicheng Zhu

Research output: Chapter in Book/Entry/PoemConference contribution

9 Scopus citations

Abstract

The highly networked and real-time data analysis features of smart manufacturing systems (SMS) require different information infrastructure, data analytics technology, and performance assurance methodologies. The main purpose of this paper is to (i) explore the complete product-process performance assurance space to identify the key performance indicators that help evaluate and quantify system performance at different abstraction levels, (ii) discuss models and methodologies for data analytics, and (iii) suggest a digital factory-based simulation technique to evaluate those key indicators for performance prediction. The paper presents a systematic and rigorous approach towards establishing these performance assurance methodologies applicable to complex value chains of smart manufacturing systems by extensively exploring all possible product and process related performance issues. A hypercube information model is proposed for the purpose of formal representation of the highly dimensional and correlated information among different actors in a smart manufacturing system, thus providing a rigorous foundation for the performance assurance space. The relevant taxonomy and an ontology-based framework are then developed for formal representation of the entities, activities and knowledge involved in the performance assurance domain. It provides a detailed insight into the PA space and defines appropriate measures which can be applied to predict and improve system performance assurances.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
EditorsJimmy Lin, Jian Pei, Xiaohua Tony Hu, Wo Chang, Raghunath Nambiar, Charu Aggarwal, Nick Cercone, Vasant Honavar, Jun Huan, Bamshad Mobasher, Saumyadipta Pyne
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1015-1023
Number of pages9
ISBN (Electronic)9781479956654
DOIs
StatePublished - 2014
Event2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States
Duration: Oct 27 2014Oct 30 2014

Publication series

NameProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

Other

Other2nd IEEE International Conference on Big Data, IEEE Big Data 2014
Country/TerritoryUnited States
CityWashington
Period10/27/1410/30/14

Keywords

  • Digital factory
  • Hypercube
  • Ontology
  • Performance assurance
  • Smart manufacturing

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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