停工期
计算机科学
方位(导航)
可靠性工程
选择(遗传算法)
工程类
机器学习
人工智能
作者
Huiyu Liu,Zhenling Mo,Heng Zhang,Xiaoming Zeng,Yunlong Wang,Qiang Miao
出处
期刊:Prognostics and System Health Management Conference
日期:2018-10-01
被引量:10
标识
DOI:10.1109/phm-chongqing.2018.00175
摘要
Rolling bearings are critical components in rotating machinery. Their failure can result in unexpected downtime and productivity reduction. Remaining useful life prediction of rolling bearing has aroused extensive attention, since it can avoid failure risks and improve stability and security of operation. This paper attempts to summarize various methods of bearing remaining useful life prediction which can be roughly classified into three kinds: physical model-based methods, statistical methods and condition monitoring data-driven methods. By comparing the advantages and disadvantages of each kind of these methods, some advice is given for prediction method selection in practical application. This paper is expected to provide a preliminary understanding of various bearing remaining useful life prediction methods.
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