Damage‐sensitive feature extraction with stacked autoencoders for unsupervised damage detection-Reference-Cited by-同舟云学术

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Damage‐sensitive feature extraction with stacked autoencoders for unsupervised damage detection

Published:2021-02-23 Issue:5 Volume:28 Page:
ISSN:1545-2255
Container-title:Structural Control and Health Monitoring
language:en
Short-container-title:Struct Control Health Monit

Author:

Silva Moisés Felipe¹^ORCID,Santos Adam¹²,Santos Reginaldo¹,Figueiredo Eloi³⁴^ORCID,Costa João C.W.A.¹

Affiliation:

1. Applied Electromagnetism Laboratory Universidade Federal do Pará Belém Brazil

2. Faculty of Computing and Electrical Engineering Universidade Federal do Sul e Sudeste do Pará Marabá Brazil

3. Faculty of Engineering Lusófona University Lisbon Portugal

4. CONSTRUCT, Faculdade de Engenharia, Universidade do Porto Porto Portugal

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Wiley

Subject

Mechanics of Materials,Building and Construction,Civil and Structural Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/stc.2714

Reference69 articles.

1. Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks

2. Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis

3. Analysis of Permanent Magnet Synchronous Motor Fault Diagnosis Based on Learning

4. Experimental validation of gas leak detection in screw thread connections of galvanized pipe based on acoustic emission and neural network

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