Web-controlled wireless network sensors for structural health monitoring

被引:7
作者
Mitchell, K [1 ]
Dang, N [1 ]
Liu, PX [1 ]
Rao, VS [1 ]
Pottinger, HJ [1 ]
机构
[1] Univ Missouri, Dept Elect & Comp Engn, Intelligent Syst Ctr, Rolla, MO 65409 USA
来源
SMART STRUCTURES AND MATERIALS 2001: SMART ELECTRONICS AND MEMS | 2001年 / 4334卷
关键词
wireless network sensors; web-control network; structural health monitoring;
D O I
10.1117/12.436606
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Wireless network sensors are being implemented for applications in transportation, manufacturing, security, and structural health monitoring. This paper describes an approach for data acquisition for damage detection in structures. The proposed Web-Controlled Wireless Network Sensors (WCWNS) is the integration of wireless network sensors and a web interface that allows easy remote access and operation from user-friendly HTML screens. The WCWNS is highly flexible in terms of functions and applications. Algorithms and tools for data analysis can be directly installed on and executed from the web server. This means WCWNS will have unlimited capabilities in performing data analysis. Data can be analyzed for damage detection either on site distributed amongst the intelligent sensors or off site either in the web server or at an end users location after downloading from the web server. This feature allows for a variety of health monitoring algorithms to be investigated by researchers of all backgrounds and abilities. In addition, both short-range and long-range communications devices handle data exchange and communications in WCWNS. The system can be setup to operate efficiently in any, al arrangement. Short-range communications devices facilitate fast and low-power local data transfer, while long-range communications devices support high quality long-distance data exchange. The proposed system is demonstrated on an experimental setup.
引用
收藏
页码:234 / 243
页数:10
相关论文
共 9 条
[1]   OPTIMAL WEIGHTED ORTHOGONALIZATION OF MEASURED MODES [J].
BARUCH, M ;
BARITZHACK, IY .
AIAA JOURNAL, 1978, 16 (04) :346-351
[2]  
Demuth H, 1997, NEURAL NETWORK TOOLB
[3]  
Doebling S.W., 1996, DAMAGE IDENTIFICATIO, DOI [10.2172/249299, DOI 10.2172/249299]
[4]  
Hagan MT., 1996, NEURAL NETWORK DESIG
[5]   STRUCTURAL DAMAGE ASSESSMENT USING A GENERALIZED MINIMUM RANK PERTURBATION-THEORY [J].
KAOUK, M ;
ZIMMERMAN, DC .
AIAA JOURNAL, 1994, 32 (04) :836-842
[6]  
LJUNG L, 1997, USERS GUIDE SYSTEM I
[7]  
O'Callahan J.C., 1989, P 7 INT MOD AN C LAS, P17
[8]  
OVERSCHEE PV, 1994, AUTOMATIC, V30, P61
[9]   N4SID - SUBSPACE ALGORITHMS FOR THE IDENTIFICATION OF COMBINED DETERMINISTIC STOCHASTIC-SYSTEMS [J].
VANOVERSCHEE, P ;
DEMOOR, B .
AUTOMATICA, 1994, 30 (01) :75-93