An unsupervised online anomaly detection method for metal additive manufacturing processes via a statistical time-frequency domain algorithm
| Author(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) |
|---|---|
| Medium: | journal article |
| Language(s): | English |
| Published in: | Structural Health Monitoring, September 2023, n. 3, v. 23 |
| Page(s): | 1926-1948 |
| DOI: | 10.1177/14759217231193702 |
- About this
data sheet - Reference-ID
10745641 - Published on:
28/10/2023 - Last updated on:
25/04/2024
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