From fault detection to one-class severity discrimination of 3D printers with one-class support vector machine

Chuan Li, Diego Cabrera, Fernando Sancho, Mariela Cerrada, René Vinicio Sánchez, Edgar Estupinan

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

16 Citas (Scopus)


The lack of faulty condition data reduces the feasibility of supervised learning for fault detection or fault severity discrimination in new manufacturing technologies. To deal with this issue, one-class learning arises for building binary discriminative models using only healthy condition data. However, these models have not been extrapolated to severity discrimination. This paper proposes to extend OCSVM, which is typically used for fault detection, to 3D printer fault severity discrimination. First, a set of features is extracted from a set of normal signals. An optimized OCSVM model is obtained by tuning the kernel and model hyperparameters. The resulting models are evaluated for fault detection and fault severity discrimination using a proposed performance evaluation approach. Experimental comparisons for belt-based faults in 3D printers show that the distance to the hyperplane has the information to discriminate the severity level, and its use is feasible. The proposed hyperparameter optimization technique improves the OCSVM for fault detection and severity discrimination compared to some other methods.

Idioma originalInglés
Páginas (desde-hasta)357-367
Número de páginas11
PublicaciónISA Transactions
EstadoPublicada - 2020

Nota bibliográfica

Funding Information:
Acknowledgments: The work was sponsored in part by GIDTEC Research Group of Universidad Politécnica Salesiana, the National Natural Science Foundation of China ( 51775112 ), the MoST Science and Technology Partnership Program ( KY201802006 ), the Key Project of the Chongqing Natural Science Foundation ( cstc2019jcyj-zdxmX0013 ), and the CTBU Open Project ( KFJJ2019059 )

Publisher Copyright:
© 2020 ISA

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


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