Real-time Production Performance Analysis Using Machine Degradation Signals: A Two-Machine Case

Yunyi Kang, Hao Yan, Feng Ju

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Ahstract- Machine degradation has significant impact on the production system performance. Its variation might lead to large deviation from the steady state performance of the whole system. In this work, we build up a model to estimate the long-term production performance of the two-machine-and-one-buffer production systems, given the real-time machine degradation signals. A phase-type distribution is generated to mimic the remaining life distribution of each machine given the degradation signal. Then a continuous Markovian model is formulated to predict longterm system throughput rate for a two-machine-and-one-buffer system. With the fluctuation of machine degradation signals, such a model can effectively estimate the expected system performance in real-time.

Original languageEnglish (US)
Title of host publication2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018
PublisherIEEE Computer Society
Pages1501-1506
Number of pages6
ISBN (Electronic)9781538635933
DOIs
StatePublished - Dec 4 2018
Event14th IEEE International Conference on Automation Science and Engineering, CASE 2018 - Munich, Germany
Duration: Aug 20 2018Aug 24 2018

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2018-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Other

Other14th IEEE International Conference on Automation Science and Engineering, CASE 2018
CountryGermany
CityMunich
Period8/20/188/24/18

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Kang, Y., Yan, H., & Ju, F. (2018). Real-time Production Performance Analysis Using Machine Degradation Signals: A Two-Machine Case. In 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018 (pp. 1501-1506). [8560385] (IEEE International Conference on Automation Science and Engineering; Vol. 2018-August). IEEE Computer Society. https://doi.org/10.1109/COASE.2018.8560385