Intelligent fault diagnosis of 3D printers based on reservoir computing

Xiang Duan, Jianyu Long, Chuan Li, Diego Cabrera, Shaohui Zhang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Fault diagnosis is important for the working conditions of 3D printers, because the failure of 3D printers will have a great impact on the quality of printed products and result in unqualified printing. In this paper, the reservoir computing (RC) method and the data collected by the attitude sensor are analyzed to obtain the health status of a 3D printer. Considering the economics and viability of fault diagnosis, a low-cost attitude sensor is installed on the moving platform of the 3D printer to collect tri-axial angular velocity, tri-axial acceleration, and tri-axial magnetic field strength signals. Then, the collected data is divided into training data and test data. The training data is used to establish the optimization parameter of the RC model to improve its performance, and the test data is used to identify the failure patterns using the model. Finally, compared with the SAE and SVM intelligent diagnosis techniques, the RC method achieves the best fault recognition accuracy, which further proves its superiority.

Original languageEnglish
Pages (from-to)3171-3178
Number of pages8
JournalInternational Journal of Performability Engineering
Volume15
Issue number12
DOIs
StatePublished - Dec 2019

Bibliographical note

Funding Information:
This work is supported in part by the National Natural Science Foundation of China (No. 51775112, 51605406), the Natural Sciences Foundation of Guangdong in China (No. 2017A030313690), and the Research Start-up Funds of DGUT (No. GC300501-12, GC300501-26).

Publisher Copyright:
© 2019 Totem Publisher, Inc. All rights reserved.

Keywords

  • 3D printer
  • Fault diagnosis
  • Pattern recognition
  • Reservoir computing

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