Fusing convolutional generative adversarial encoders for 3D printer fault detection with only normal condition signals

Chuan Li, Diego Cabrera, Fernando Sancho, René Vinicio Sánchez, Mariela Cerrada, Jianyu Long, José Valente de Oliveira

Research output: Contribution to journalArticlepeer-review

2 Scopus citations
Original languageEnglish
Article number107108
JournalMechanical Systems and Signal Processing
Volume147
DOIs
StatePublished - 15 Jan 2021

Bibliographical note

Funding Information:
The work was sponsored in part by GIDTEC Research Group of Universidad Politécnica Salesiana, the National Natural Science Foundation of China (51775112, 71801046), the National Key R&D Program (2016YFE0132200), the MoST Science and Technology Partnership Program (KY201802006), the Chongqing Natural Science Foundation (cstc2019jcyj-zdxmX0013), and the CTBU Project (KFJJ2018107, KFJJ2018075).

Publisher Copyright:
© 2020 Elsevier Ltd

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • 3D printers
  • Adversarial learning
  • Condition-based maintenance
  • Convolutional neural networks
  • Fault detection

Fingerprint Dive into the research topics of 'Fusing convolutional generative adversarial encoders for 3D printer fault detection with only normal condition signals'. Together they form a unique fingerprint.

Cite this