Low-cost and Small-sample Fault Diagnosis for 3D Printers Based on Echo State Networks

Kun He, Lianghua Zeng, Qin Shui, Jianyu Long, Chuan Li, Diego Cabrera

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

With the 3D printing rapidly expanding into various fields, 3D printers, as the equipment, should adopt a low-cost and small-sample fault diagnosis methods. A fault diagnosis method based on echo state networks (ESN) for 3D printers is proposed in this paper. A low-cost attitude sensor installed on the 3D printer is employed to collect raw fault data. Subsequently, feature extraction is carried out on the raw fault data. Using these features, ESN, as a shallow learning network, is modeled to diagnose faults of 3D printers. Experimental results show that the fault diagnosis method based on ESN still effective for 3D printers in low-cost and small-sample, which can make the fault recognition accuracy of 3D printer reach to 97.26%. Furthermore, contrast results indicated that the fault diagnosis accuracy of ESN is higher and most stable when compare with support vector machine (SVM), locality preserving projection support vector machine (LPPSVM) and principal component analysis support vector machine (PCASVM).

Original languageEnglish
Title of host publication2019 Prognostics and System Health Management Conference, PHAI-Qingdao 2019
EditorsWei Guo, Steven Li, Qiang Miao
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728108612
DOIs
StatePublished - Oct 2019
Event10th Prognostics and System Health Management Conference, PHM-Qingdao 2019 - Qingdao, China
Duration: 25 Oct 201927 Oct 2019

Publication series

Name2019 Prognostics and System Health Management Conference, PHM-Qingdao 2019

Conference

Conference10th Prognostics and System Health Management Conference, PHM-Qingdao 2019
Country/TerritoryChina
CityQingdao
Period25/10/1927/10/19

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work is supported in part by the National Natural Science Foundation of China (71801046, 51605406 and 51775112), and the Research Program of Higher Education of Guangdong (2016KZDXM054, 2018KQNCX343).

Publisher Copyright:
© 2019 IEEE.

Keywords

  • 3D printer
  • echo state networks
  • fault diagnosis
  • feature extraction
  • machine learning

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