An unsupervised online anomaly detection method for metal additive manufacturing processes via a statistical time-frequency domain algorithm
| Auteur(s): |
Alvin Chen
(Intelligent Structural Systems Laboratory, Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA)
Fotis Kopsaftopoulos
(Intelligent Structural Systems Laboratory, Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA)
Sandipan Mishra (Intelligent Structural Systems Laboratory, Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA) |
|---|---|
| Médium: | article de revue |
| Langue(s): | anglais |
| Publié dans: | Structural Health Monitoring, septembre 2023, n. 3, v. 23 |
| Page(s): | 1926-1948 |
| DOI: | 10.1177/14759217231193702 |
- Informations
sur cette fiche - Reference-ID
10745641 - Publié(e) le:
28.10.2023 - Modifié(e) le:
25.04.2024
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