Hierarchical outlier detection approach for online distributed structural identification-Reference-Cited by-同舟云学术

Hierarchical outlier detection approach for online distributed structural identification

Published:2020-08-10 Issue:11 Volume:27 Page:
ISSN:1545-2255
Container-title:Structural Control and Health Monitoring
language:en
Short-container-title:Struct Control Health Monit

Author:

Huang Ke¹^ORCID,Yuen Ka‐Veng¹^ORCID

Affiliation:

1. State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering University of Macau Macao 999078 China

Funder

Universidade de Macau

Fundo para o Desenvolvimento das Ciências e da Tecnologia

Publisher

Wiley

Subject

Mechanics of Materials,Building and Construction,Civil and Structural Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/stc.2623

Reference63 articles.

1. Pareto optimal sensor locations for structural identification

2. Dynamic reduction-based structural damage detection of transmission tower utilizing ambient vibration data

3. Statistical detection of multiple cracks on thin plates utilizing dynamic response

4. A Bayesian Probabilistic Approach for Crack Characterization in Plate Structures

5. Statistic structural damage detection based on the closed-form of element modal strain energy sensitivity

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Rail crack identification of in-service turnout through Bayesian inference;Journal of Vibration and Control;2024-07-24

2. Online meta-learning approach for sensor fault diagnosis using limited data;Smart Materials and Structures;2024-07-09

3. Abnormal data detection and recovery of sensors network based on spatiotemporal deep learning methodology;Measurement;2024-03

4. Damage Identification of Turnout Rail through a Covariance-Based Condition Index and Quantitative Pattern Analysis;Infrastructures;2023-12-08

5. A Unified Bayesian Framework for Joint Estimation and Anomaly Detection in Environmental Sensor Networks;IEEE Access;2023