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

Autor: Li C; National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China., Cabrera D; National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China; GIDTEC, Universidad Politécnica Salesiana, Ecuador. Electronic address: dcabrera@ups.edu.ec., Sancho F; Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, Spain., Cerrada M; GIDTEC, Universidad Politécnica Salesiana, Ecuador., Sánchez RV; GIDTEC, Universidad Politécnica Salesiana, Ecuador., Estupinan E; Department of Mechanical Engineering, University of Tarapaca, Arica, Chile.
Jazyk: angličtina
Zdroj: ISA transactions [ISA Trans] 2021 Apr; Vol. 110, pp. 357-367. Date of Electronic Publication: 2020 Oct 15.
DOI: 10.1016/j.isatra.2020.10.036
Abstrakt: 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.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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