An unsupervised online anomaly detection method for metal additive manufacturing processes via a statistical time-frequency domain algorithm
Autor(en): |
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) |
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Medium: | Fachartikel |
Sprache(n): | Englisch |
Veröffentlicht in: | Structural Health Monitoring, September 2023, n. 3, v. 23 |
Seite(n): | 1926-1948 |
DOI: | 10.1177/14759217231193702 |
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Datenseite - Reference-ID
10745641 - Veröffentlicht am:
28.10.2023 - Geändert am:
25.04.2024