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

Producción científica: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

4 Citas (Scopus)

Resumen

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).

Idioma originalInglés
Título de la publicación alojada2019 Prognostics and System Health Management Conference, PHAI-Qingdao 2019
EditoresWei Guo, Steven Li, Qiang Miao
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728108612
DOI
EstadoPublicada - oct. 2019
Evento10th Prognostics and System Health Management Conference, PHM-Qingdao 2019 - Qingdao, China
Duración: 25 oct. 201927 oct. 2019

Serie de la publicación

Nombre2019 Prognostics and System Health Management Conference, PHM-Qingdao 2019

Conferencia

Conferencia10th Prognostics and System Health Management Conference, PHM-Qingdao 2019
País/TerritorioChina
CiudadQingdao
Período25/10/1927/10/19

Nota bibliográfica

Publisher Copyright:
© 2019 IEEE.

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