Name
Abstract | ||
|---|---|---|
Wireless sensor networks, which enjoy increasing interests in the field of personal and ubiquitous computing, have recently been regarded as a promising candidate for structural health monitoring. This paper proposes a neural-network based structural health monitoring scheme by using wireless sensor networks, in which hundreds of sensor nodes perform distributed sensing and collaborative computing for structural health analysis. Since vibration frequencies of architectural or mechanical structures imply their health status, structural health problems can be detected through processing vibration frequency data collected by sensors. Artificial neural-network based machine learning algorithm is then employed for data processing. After constructing samples of training data in healthy and unhealthy status respectively, data coming from sensors are tested and classified into a certain category which indicates health status of monitoring structure. Simulation results demonstrate that the proposed scheme achieves not only higher accuracy of structural health monitoring but also more robust performance against environmental noises and interferences, compared with some existing methods. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICNC.2013.6817963 | ICNC |
Keywords | Field | DocType |
sensor nodes,neural-network,learning (artificial intelligence),vibrations,wireless sensor network,vibration frequency processing,neural-network based structural health monitoring scheme,data processing,structural engineering computing,distributed sensing,ubiquitous computing,structural health monitoring,condition monitoring,machine learning algorithm,wireless sensor networks,neural nets,noise,support vector machines,learning artificial intelligence,accuracy,neural network | Data mining,Data processing,Computer science,Visual sensor network,Real-time computing,Artificial intelligence,Ubiquitous computing,Artificial neural network,Key distribution in wireless sensor networks,Structural health monitoring,Mobile wireless sensor network,Wireless sensor network,Machine learning | Conference |
Citations | PageRank | References |
3 | 0.46 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaobo Xie | 1 | 25 | 6.81 |
| Junqi Guo | 2 | 61 | 15.07 |
| Hongyang Zhang | 3 | 68 | 7.68 |
| Tao Jiang | 4 | 3 | 0.46 |
| Rongfang Bie | 5 | 547 | 68.23 |
| Yunchuan Sun | 6 | 534 | 54.06 |