期刊
ISA TRANSACTIONS
卷 110, 期 -, 页码 357-367出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2020.10.036
关键词
Fault detection; Severity discrimination; One-class support vector machine; 3D printer; Bidirectional generative adversarial network
资金
- GIDTEC Research Group of Universidad Politecnica Salesiana
- National Natural Science Foundation of China [51775112]
- MoST Science and Technology Partnership Program [KY201802006]
- Key Project of the Chongqing Natural Science Foundation [cstc2019jcyj-zdxmX0013]
- CTBU Open Project [KFJJ2019059]
This paper proposes an extension of OCSVM for fault severity discrimination in 3D printers, improving fault detection and severity discrimination through model optimization. Experimental results show that the distance to the hyperplane is informative for severity discrimination.
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. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.
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