Deep Laplacian Auto-encoder and its application into imbalanced fault diagnosis of rotating machinery-Reference-Cited by-同舟云学术

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Deep Laplacian Auto-encoder and its application into imbalanced fault diagnosis of rotating machinery

Published:2020-02 Issue: Volume:152 Page:107320
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Zhao Xiaoli,Jia Minping,Lin Mingyao

Funder

State Key Program of National Natural Science Foundation of China

National Natural Science Foundation of China

Postgraduate Research & Practice Innovation Program of Jiangsu Province, China

China Scholarship Council

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference59 articles.

1. Prognostics and health management design for rotary machinery systems—reviews, methodology and applications;Lee;Mech. Syst. Signal Process.,2014

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

3. Sparse deep stacking network for fault diagnosis of motor;Sun;IEEE Trans. Ind. Electron.,2018

4. A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM;Zhang;Measurement,2015

5. A combined polynomial chirplet transform and synchroextracting technique for analyzing nonstationary signals of rotating machinery;Yu;IEEE Trans. Instrum. Meas.,2019

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