Name
Title | ||
|---|---|---|
On Structural Health Monitoring Using Tensor Analysis and Support Vector Machine with Artificial Negative Data |
Abstract | ||
|---|---|---|
Structural health monitoring is a condition-based technology to monitor infrastructure using sensing systems. Since we usually only have data associated with the healthy state of a structure, one-class approaches are more practical. However, tuning the parameters for one-class techniques (like one-class Support Vector Machines) still remains a relatively open and difficult problem. Moreover, in structural health monitoring, data are usually highly redundant and correlated, which a matrix-based approach cannot 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 propose the use of tensor learning and support vector machines with artificial negative data generated by density estimation techniques for damage detection, localization and estimation in a one-class manner. The artificial negative data can help tuning SVM parameters and calibrating probabilistic outputs, which is not possible to do with one-class SVM. The proposed method shows promising results using data from laboratory-based structures and also with data collected from the Sydney Harbour Bridge, one of the most iconic structures in Australia. The method works better than the one-class approach and the approach without using tensor analysis. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2983323.2983359 | ACM International Conference on Information and Knowledge Management |
Keywords | Field | DocType |
tensor analysis,damage identification,artificial negative data,density estimation,support vector machine | Density estimation,Data mining,Tensor,Structural health monitoring,Pattern recognition,Matrix (mathematics),Computer science,Support vector machine,Artificial intelligence,Probabilistic logic,Vibration,Calibration | Conference |
Citations | PageRank | References |
3 | 0.40 | 11 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Prasad Cheema | 1 | 3 | 0.40 |
| Nguyen Lu Dang Khoa | 2 | 62 | 8.44 |
| Mehrisadat Makki Alamdari | 3 | 8 | 1.86 |
| Wei Liu | 4 | 468 | 37.36 |
| Yang Wang | 5 | 9 | 6.83 |
| Fang Chen | 6 | 156 | 49.84 |
| Peter Runcie | 7 | 9 | 0.98 |