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

28 Scopus citations


Collecting data from mechanical systems in abnormal conditions is expensive and time consuming. Consequently, fault detection approaches based on classical supervised learning working with both normal and abnormal data are not applicable in some condition-based maintenance tasks. To address this problem, this paper proposes Fusing Convolutional Generative Adversarial Encoders (fCGAE) method to create fault detection models from only normal data. Firstly, to obtain an adequate deep feature space, encoder models based on 1D convolutional neural networks are created. Then, these encoders are optimized in an unsupervised way through Bidirectional Generative Adversarial Networks. Finally, the multi-channel features collected from the system are merged with One-Class Support Vector Machine. fCGAE is applied to fault detection in 3D printers, where experimental results in two fault detection cases show excellent generalization capabilities and better performance compared to peer methods.

Original languageEnglish
Article number107108
JournalMechanical Systems and Signal Processing
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 2020 Elsevier B.V., All rights reserved.


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


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