A new deep transfer learning network based on convolutional auto-encoder for mechanical fault diagnosis-Reference-Cited by-同舟云学术

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A new deep transfer learning network based on convolutional auto-encoder for mechanical fault diagnosis

Published:2021-06 Issue: Volume:178 Page:109352
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Qian Quan,Qin Yi,Wang Yi,Liu Fuqiang

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

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

Reference37 articles.

1. Multi-scale transfer voting mechanism: a new strategy for domain adaption;Qin;IEEE Trans. Ind. Inf.,2020

2. Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals;Glowacz;Measurement,2017

3. Macroscopic-Microscopic Attention in LSTM Networks Based on Fusion Features for Gear Remaining Life Prediction;Qin;IEEE Trans. Ind. Electron.,2020

4. A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings;Liu;Measurement,2020

5. Fault diagnosis of multi-channel data by the CNN with the multilinear principal component analysis;Guo;Measurement,2021

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