Rolling bearing fault detection using continuous deep belief network with locally linear embedding-Reference-Cited by-同舟云学术

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Rolling bearing fault detection using continuous deep belief network with locally linear embedding

Published:2018-04 Issue: Volume:96 Page:27-39
ISSN:0166-3615
Container-title:Computers in Industry
language:en
Short-container-title:Computers in Industry

Author:

Shao Haidong,Jiang Hongkai,Li Xingqiu,Liang Tianchen

Funder

National Natural Science Foundation of China

Shanghai Engineering Research Center of Civil Aircraft Health Monitoring Foundation of China

Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University

Publisher

Elsevier BV

Subject

General Engineering,General Computer Science

Reference54 articles.

1. An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis;Jiang;Mech. Syst. Signal Process.,2013

2. Research on assembly quality adaptive control system for complex mechanical products assembly process under uncertainty;Wang;Comput. Ind.,2015

3. Rotating machinery prognostics: state of the art, challenges and opportunities;Heng;Mech. Syst. Signal Process.,2009

4. Data quality evaluation and improvement for prognostic modeling using visual assessment based data partitioning method;Chen;Comput. Ind.,2013

5. A novel intelligent method for bearing fault diagnosis based on affinity propagation clustering and adaptive feature selection;Wei;Knowl.-Based Syst.,2017

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