Name
Abstract | ||
|---|---|---|
Structure health monitoring aims to detect the nature of structure damage by using a network of sensors, whose sensor signals are highly correlated and mixed with noise, it is difficult to identify direct relationship between sensors and abnormal structure characteristics. In this study, we apply sensor sensitivity analysis on a structure damage identifier, which integrates independent component analysis (ICA) and support vector machine (SVM) together. The approach is evaluated on a benchmark data from University of British Columbia. Experimental results show sensitivity analysis not only helps domain experts understand the mapping from different location and type of sensors to a damage class, but also significantly reduce noise and improve the accuracy of different level damages identification. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11881599_164 | FSKD |
Keywords | Field | DocType |
structure damage,independent component analysis,different location,structure damage identifier,sensitivity analysis,structure damage identification,damage class,sensor sensitivity analysis,different level damages identification,abnormal structure characteristic,structure health monitoring,support vector machine | Pattern recognition,Identifier,Computer science,Sensor array,Fuzzy logic,Support vector machine,Artificial intelligence,Knowledge extraction,Independent component analysis,Abnormal structure,Statistical analysis | Conference |
Volume | ISSN | ISBN |
4223 | 0302-9743 | 3-540-45916-2 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huazhu Song | 1 | 17 | 6.88 |
| Luo Zhong | 2 | 22 | 7.33 |
| Bo Han | 3 | 6 | 1.53 |