From fault detection to one-class severity discrimination of 3D printers with one-class support vector machine
Abstract:
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.
Año de publicación:
2021
Keywords:
- Fault Detection
- Severity discrimination
- One-Class Support Vector Machine
- 3D printer
- Bidirectional generative adversarial network
Fuente:


Tipo de documento:
Article
Estado:
Acceso restringido
Áreas de conocimiento:
- Aprendizaje automático
- Ciencias de la computación
Áreas temáticas:
- Física aplicada
- Métodos informáticos especiales
- Instrumentos de precisión y otros dispositivos