Name
Abstract | ||
|---|---|---|
Structural health monitoring is a condition-based technology to monitor infrastructure using sensing systems. In structural health monitoring, the data are usually highly redundant and correlated. The measured variables are not only correlated with each other at a certain time but also are autocorrelated themselves over time. Matrix-based two-way analysis, which is usually used in structural health monitoring, can not capture all these relationships and correlations together. Tensor analysis allows us to analyse the vibration data in temporal, spatial and feature modes at the same time. In our approach, we use tensor analysis and one-class support vector machine for damage detection, localization and estimation in an unsupervised manner. The method shows promising results using data from lab-based structures and also data collected from the Sydney Harbour Bridge, one of iconic structures in Australia. We can obtain a damage detection accuracy of 0.98 and higher for all the data. Locations of damage were captured correctly and different levels of damage severity were well estimated. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-18038-0_36 | ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PART I |
Keywords | Field | DocType |
Tensor analysis,Structural health monitoring,Damage identification,Unsupervised learning | Data mining,Sensing system,Structural health monitoring,Tensor,Matrix (mathematics),Computer science,Support vector machine,Unsupervised learning,Vibration,Autocorrelation | Conference |
Volume | ISSN | Citations |
9077 | 0302-9743 | 6 |
PageRank | References | Authors |
0.57 | 7 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nguyen Lu Dang Khoa | 1 | 62 | 8.44 |
| Bang Zhang | 2 | 111 | 12.40 |
| Yang Wang 0002 | 3 | 10 | 2.79 |
| Wei Liu | 4 | 468 | 37.36 |
| Fang Chen | 5 | 156 | 49.84 |
| Samir Mustapha | 6 | 9 | 1.65 |
| Peter Runcie | 7 | 9 | 0.98 |