Application of Online Anomaly Detection Using One-Class Classification to the Z24 Bridge
The usage of anomaly detection is of critical importance to numerous domains, including structural health monitoring (SHM). In this study, we examine an online setting for damage detection in the Z24 bridge. We evaluate and compare the performance of the elliptic envelope, incremental one-class supp...
Saved in:
| Main Author: | Amro Abdrabo |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/23/7866 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Online Machine Learning for Intrusion Detection in Electric Vehicle Charging Systems
by: Fazliddin Makhmudov, et al.
Published: (2025-02-01) -
ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
by: Danlei Li, et al.
Published: (2025-06-01) -
Analysis of Descriptors of Concept Drift and Their Impacts
by: Albert Costa, et al.
Published: (2025-01-01) -
Concept Drift Detection Based on Deep Neural Networks and Autoencoders
by: Lisha Hu, et al.
Published: (2025-03-01) -
Self-Supervised Drift-Resilient Classification for Time Series Industrial Anomaly Detection
by: Myung-Kyo Seo, et al.
Published: (2025-01-01)