Transfer learning for bearing performance degradation assessment based on deep hierarchical features-Reference-Cited by-同舟云学术

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Transfer learning for bearing performance degradation assessment based on deep hierarchical features

Published:2021-02 Issue: Volume:108 Page:343-355
ISSN:0019-0578
Container-title:ISA Transactions
language:en
Short-container-title:ISA Transactions

Author:

Dong Shuzhi,Wen Guangrui,Lei Zihao,Zhang Zhifen

Funder

National Key Research and Development Project

National Natural Science Foundation

Equipment Pre-research Fund of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Control and Systems Engineering,Electrical and Electronic Engineering,Computer Science Applications,Instrumentation

Reference29 articles.

1. A review on data-driven fault severity assessment in rolling bearings;Cerrada;Mech Syst Signal Process,2018

2. Distribution adaptation and manifold alignment for complex processes fault diagnosis;Wang;Knowl-Based Syst,2018

3. Research on condition monitoring of bearing health using vibration data;Ma;Appl Mech Mater,2012

4. Rolling bearing fault evolution based on vibration time-domain parameters;Wu;Key Eng Mater,2016

5. Bearing degradation process prediction based on the PCA and optimized LS-SVM model;Dong;Measurement,2013

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