Planetary gear fault diagnosis using stacked denoising autoencoder and gated recurrent unit neural network under noisy environment and time-varying rotational speed conditions-Reference-Cited by-同舟云学术

Planetary gear fault diagnosis using stacked denoising autoencoder and gated recurrent unit neural network under noisy environment and time-varying rotational speed conditions

Published:2019-07-19 Issue:9 Volume:30 Page:095003
ISSN:0957-0233
Container-title:Measurement Science and Technology
language:
Short-container-title:Meas. Sci. Technol.

Author:

Yu Jun^ORCID,Xu Yonggang,Liu Ke

Funder

National Natural Science Foundation of China

Distinguished Young Scientists Funds of Heilongjiang Province

National Key Research and Development Plan

Publisher

IOP Publishing

Subject

Applied Mathematics,Instrumentation,Engineering (miscellaneous)

Link

http://iopscience.iop.org/article/10.1088/1361-6501/ab1da0/pdf

Reference40 articles.

1. A fault diagnosis method for planetary gearboxes under non-stationary working conditions using improved Vold-Kalman filter and multi-scale sample entropy

2. Gearbox condition monitoring in wind turbines: A review

3. A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management

4. A new adaptive evolutionary digital filter based on alternately evolutionary rules for fault detection of gear tooth spalling

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