Performance Evaluation of Production Systems Using Real-Time Machine Degradation Signals

Yunyi Kang, Hao Yan, Feng Ju

Research output: Contribution to journalArticle

Abstract

A machine's degradation status directly influences the operational performance of the production system, such as productivity and product quality. For example, machines associated with different health states may have different remaining life before failure, thus impacting the system throughput. Therefore, it is critical to analyze the coupling between the overall system performance and the machine degradation to better production decision-making, such as maintenance and product dispatch decisions. In this paper, we propose a novel model to evaluate the production performance of a two-machine-and-one-buffer line, given the real-time machine degradation signals. Specifically, a phase-type distribution-based continuous-time Markov chain model is formulated to estimate the system throughput by utilizing the remaining life prediction from the degradation signals. A case study is provided to demonstrate the applicability and effectiveness of the proposed method.

Original languageEnglish (US)
JournalIEEE Transactions on Automation Science and Engineering
DOIs
StateAccepted/In press - Jan 1 2019
Externally publishedYes

Fingerprint

Real time systems
Degradation
Throughput
Markov processes
Productivity
Decision making
Health

Keywords

  • Machine degradation
  • Markov chain
  • performance evaluation
  • remaining life.

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

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