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An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings

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成果类型:
期刊论文
作者:
Peng, Yanfeng;Cheng, Junsheng*;Liu, Yanfei;Li, Xuejun;Peng, Zhihua
通讯作者:
Cheng, Junsheng
作者机构:
[Cheng, Junsheng; Peng, Yanfeng] Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China.
[Cheng, Junsheng; Peng, Yanfeng] Hunan Univ, Coll Mech & Vehicle Engn, Changsha 410082, Hunan, Peoples R China.
[Liu, Yanfei; Li, Xuejun; Peng, Yanfeng] Hunan Univ Sci & Technol, Hunan Prov Key Lab Hlth Maintenance Mech Equipmen, Xiangtan 411201, Peoples R China.
[Peng, Zhihua] Univ South China, Sch Math & Phys, Hengyang 421001, Peoples R China.
通讯机构:
[Cheng, Junsheng] H
Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China.
语种:
英文
关键词:
Gaussian mixture model;distance evaluation technique;health state;remaining useful life;rolling bearing
关键词(中文):
数据驱动;预言;轴承;生活;数据集合;健康状态;集合划分
期刊:
机械工程前沿
ISSN:
2095-0233
年:
2018
卷:
13
期:
2
页码:
301-310
基金类别:
Acknowledgements The authors gratefully acknowledge the support of the National Key Research and Development Program of China (Grant No. 2016YFF0203400), the National Natural Science Foundation of China (Grant Nos. 51575168 and 51375152), the Project of National Science and Technology Supporting Plan (Grant No. 2015BAF32B03), and the Science Research Key Program of Educational Department of Hunan Province of China (Grant No. 16A180). The authors appreciate the support provided by the Collaborative Innovation Center of Intelligent New Energy Vehicle, the Hunan Collaborative Innovation Center for Green Car.
机构署名:
本校为其他机构
院系归属:
数理学院
摘要:
A novel data-driven method based on Gaussian mixture model (GMM) and distance evaluation technique (DET) is proposed to predict the remaining useful life (RUL) of rolling bearings. The data sets are clustered by GMM to divide all data sets into several health states adaptively and reasonably. The number of clusters is determined by the minimum description length principle. Thus, either the health state of the data sets or the number of the states is obtained automatically. Meanwhile, the abnormal data sets can be recognized during the clustering process and removed from the training data sets....

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