Distance-Based Multivariate Anomaly Detection in Wire Arc Additive Manufacturing
Reisch R, Hauser T, Lutz B, Pantano M, Kamps T, Knoll A (2020)
Publication Type: Conference contribution
Publication year: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 659-664
Conference Proceedings Title: Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020
ISBN: 9781728184708
DOI: 10.1109/ICMLA51294.2020.00109
Abstract
Wire Arc Additive Manufacturing (WAAM) offers the possibility to build up large-scale metal parts. Data which is obtained from a multivariate sensor system in-situ must be analyzed automatically to ensure an early and reliable detection of defects to reduce the costs due to production scrap. For that reason, a modular anomaly detector for multivariate time series in WAAM was investigated in this paper. The approach adressed major topics in real-life data sets of industrial applications such as miscellaneous signal sample rates, lack of synchronization and concept drift. A reference data set based on an anomaly-dependently splitted time horizon was defined to reduce the sensitivity loss of the detector after an anomaly. To avoid the need for labeled data, an unsupervised anomaly detection method based on neural networks was used. Hence, no time and costs for artificial defect creation on the machine tool are required when implementing the approach in industrial applications.
Authors with CRIS profile
Involved external institutions
Germany (DE)How to cite
APA:
Reisch, R., Hauser, T., Lutz, B., Pantano, M., Kamps, T., & Knoll, A. (2020). Distance-Based Multivariate Anomaly Detection in Wire Arc Additive Manufacturing. In M. Arif Wani, Feng Luo, Xiaolin Li, Dejing Dou, Francesco Bonchi (Eds.), Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020 (pp. 659-664). Institute of Electrical and Electronics Engineers Inc..
MLA:
Reisch, Raven, et al. "Distance-Based Multivariate Anomaly Detection in Wire Arc Additive Manufacturing." Proceedings of the 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020 Ed. M. Arif Wani, Feng Luo, Xiaolin Li, Dejing Dou, Francesco Bonchi, Institute of Electrical and Electronics Engineers Inc., 2020. 659-664.
BibTeX: Download