文章基本信息

标题：Data-driven Machinery Prognostics Approach using in a Predictive Maintenance Model
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作者：Liao, Wenzhu ; Wang, Ying
期刊名称：Journal of Computers
印刷版ISSN：1796-203X
出版年度：2013
卷号：8
期号：1
页码：225-231
DOI：10.4304/jcp.8.1.225-231
语种：English
出版社：Academy Publisher
摘要：Nowadays, more and more manufacturers realize the importance of adopting new maintenance technologies to enable systems to achieve near-zero downtime, so machinery prognostics that enables this paradigm shift from traditional fail-and-fix maintenance to a predict-and-prevent paradigm has arose interests from researchers. Machinery prognostics which could estimate machine condition and degradation strongly support predictive maintenance policy. This paper develops a novel data-driven machine prognostics approach to predict machine’s health condition and describe machine degradation. Based on machine’s prognostics information, a predictive maintenance model is well constructed to decide machine’s optimal maintenance threshold and maintenance cycles. Through a case study, this predictive maintenance model is verified, and the computational results show that this proposed model is efficient and practical.
关键词：prognostics;predictive maintenance;cost;optimization