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contributor authorLee, Jay
contributor authorNi, Jun
contributor authorSingh, Jaskaran
contributor authorJiang, Baoyang
contributor authorAzamfar, Moslem
contributor authorFeng, Jianshe
date accessioned2022-02-04T22:12:19Z
date available2022-02-04T22:12:19Z
date copyright8/18/2020 12:00:00 AM
date issued2020
identifier issn1087-1357
identifier othermanu_142_11_110804.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4275091
description abstractWith continued global market growth and an increasingly competitive environment, manufacturing industry is facing challenges and desires to seek continuous improvement. This effect is forcing manufacturers to squeeze every asset for maximum value and thereby calls for high-equipment effectiveness, and at the same time flexible and resilient manufacturing systems. Maintenance operations are essential to modern manufacturing systems in terms of minimizing unplanned down time, assuring product quality, reducing customer dissatisfaction, and maintaining advantages and competitiveness edge in the market. It has a long history that manufacturers struggle to find balanced maintenance strategies without significantly compromising system reliability or productivity. Intelligent maintenance systems (IMS) are designed to provide decision support tools to optimize maintenance operations. Intelligent prognostic and health management tools are imperative to identify effective, reliable, and cost-saving maintenance strategies to ensure consistent production with minimized unplanned downtime. This article aims to present a comprehensive review of the recent efforts and advances in prominent methods for maintenance in manufacturing industries over the last decades, identifying the existing research challenges, and outlining directions for future research.
publisherThe American Society of Mechanical Engineers (ASME)
titleIntelligent Maintenance Systems and Predictive Manufacturing
typeJournal Paper
journal volume142
journal issue11
journal titleJournal of Manufacturing Science and Engineering
identifier doi10.1115/1.4047856
journal fristpage0110805-1
journal lastpage0110805-16
page16
treeJournal of Manufacturing Science and Engineering:;2020:;volume( 142 ):;issue: 011
contenttypeFulltext


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