Structural Health Monitoring With Autoregressive Support Vector Machines-Reference-Cited by-同舟云学术

Structural Health Monitoring With Autoregressive Support Vector Machines

Published:2009-02-17 Issue:2 Volume:131 Page:
ISSN:1048-9002
Container-title:Journal of Vibration and Acoustics
language:en
Short-container-title:

Author:

Bornn Luke¹,Farrar Charles R.²,Park Gyuhae²,Farinholt Kevin²

Affiliation:

1. CCS-6, Statistical Sciences Group, Los Alamos National Laboratory, MS F600, Los Alamos, NM 87545

2. The Engineering Institute, Los Alamos National Laboratory, MS T006, Los Alamos, NM 87545

Abstract

The use of statistical methods for anomaly detection has become of interest to researchers in many subject areas. Structural health monitoring in particular has benefited from the versatility of statistical damage-detection techniques. We propose modeling structural vibration sensor output data using nonlinear time-series models. We demonstrate the improved performance of these models over currently used linear models. Whereas existing methods typically use a single sensor’s output for damage detection, we create a combined sensor analysis to maximize the efficiency of damage detection. From this combined analysis we may also identify the individual sensors that are most influenced by structural damage.

Publisher

ASME International

Subject

General Engineering

Link

http://asmedigitalcollection.asme.org/vibrationacoustics/article-pdf/doi/10.1115/1.3025827/5830361/021004_1.pdf

Reference21 articles.

1. A Review of Damage Identification Methods That Examine Changes in Dynamic Properties;Doebling;Shock Vib. Dig.

2. Sohn, H., Farrar, C. R., Hemez, F. M., Shunk, D. S., Stinemates, D. W., Nadler, B. R., and Czarnecki, J. J., 2004, “A Review of Structural Health Monitoring Literature From 1996–2001,” Los Alamos National Laboratory, Report No. LA-13976-MS.

3. An Introduction to Structural Health Monitoring;Farrar;Philos. Trans. R. Soc. London, Ser. A

4. Vibration-Based Damage Detection Using Statistical Process Control;Fugate;Mech. Syst. Signal Process.

5. Structural Health Monitoring Using Statistical Process Control;Sohn;J. Struct. Eng.

Cited by 68 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. Condition Monitoring Framework for Damage Identification in CFRP Rotating Shafts using Model-Driven Machine Learning Techniques;Engineering Failure Analysis;2024-02

2. Integrating Image Processing and Machine Learning for the Non-Destructive Assessment of RC Beams Damage;Buildings;2024-01-13

3. On the use of matrix profiles and optimal transport theory for multivariate time series anomaly detection within structural health monitoring;Mechanical Systems and Signal Processing;2023-12

4. Proposed Machine Learning Techniques for Bridge Structural Health Monitoring: A Laboratory Study;Remote Sensing;2023-04-09

5. Framework for Identification and Prediction of Corrosion Degradation in a Steel Column through Machine Learning and Bayesian Updating;Applied Sciences;2023-04-06