Artículo
From fault detection to one-class severity discrimination of 3D printers with one-class support vector machine
Autor/es | Li, Chuan
Cabrera, Diego Sancho Caparrini, Fernando Cerrada, Mariela Sánchez, René-Vinicio Estupinan, Edgar |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2021 |
Fecha de depósito | 2021-04-16 |
Publicado en |
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Resumen | 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 ... 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. |
Cita | Li, C., Cabrera, D., Sancho Caparrini, F., Cerrada, M., Sánchez, R. y Estupinan, E. (2021). From fault detection to one-class severity discrimination of 3D printers with one-class support vector machine. ISA Transactions, 110 (April 2021), 357-367. |
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