Estimation of active maintenance opportunity windows in Bernoulli production lines. (October 2017)

• Record Type: Journal Article
• Authors: Gu, Xi; Jin, Xiaoning; Guo, Weihong; Ni, Jun
• Is Part Of: Journal of manufacturing systems. Volume 45(2017)
• Page Start: 109
• Page End: 120
• Publication Date: 2017-10
• Subjects: Opportunistic maintenance -- Manufacturing systems -- Markov process; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2017.08.005
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: A concept of Active Maintenance Opportunity Window (AMOW) is proposed, so that maintenance can be performed during production time without causing system-level production losses. The closed-formed expressions of AMOWs are derived for two-machine-one-buffer (2M1B) Bernoulli lines. An approximation method based on aggregation is developed to estimate AMOWs in general Bernoulli production lines. A heuristic algorithm is proposed for balanced Bernoulli lines, and the effectiveness of the algorithm is validated. Abstract: Effective and timely maintenance actions can sustain and improve both system availability and product quality in automated manufacturing systems. However, arbitrarily stopping machines for maintenance will occupy their production time and may introduce system-level production losses. There may exist hidden opportunities during production, such that specific machines can be actively shut down for preventive maintenance without penalizing the system throughput. In this paper, the time intervals for such opportunities are defined as active maintenance opportunity windows (AMOWs). A Bernoulli model is developed to analytically estimate AMOWs in two-machine-one-buffer production lines. A recursive algorithm based on an aggregation method is used to estimate AMOWs in long lines. For balanced production lines, a heuristic algorithm is proposed to estimate AMOWs in real time. The effectiveness of the methods has been validated through numerical studies.