Frontiers in sensors and sensing systems

Glaser, Steven D.; Shoureshi, R.; Pescovitz, David

doi:10.12989/sss.2005.1.1.103

Cited by 29 publications

(16 citation statements)

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“…Define the ratio of the variance of the residual errors to that in the database as (6) Here, and are variances of the residual errors. Suppose the models are accurate and the ambient excitations are Gaussian random variables with zero means.…”

Section: B Statistical Pattern Recognitionmentioning

confidence: 99%

“…In a WSN, sensor reports a damage-sensitive coefficient as defined in (6). From the discussions in the previous section, is the summation of a damage-related term and a random variable with F-distribution (7) Here, is decided by the cumulative effects of the damaged points.…”

Section: A Problem Formulationmentioning

confidence: 99%

“…The data has been shown in Fig. 3, with stiffness reduction as 57% in a single column between (6,5) and (7,5). The Threshold algorithm identifies the damages in points (6,5) and (7,5).…”

Section: B Single-damage Patternmentioning

confidence: 99%

“…The Threshold algorithm identifies the damages in points (6,5) and (7,5). The Centralized algorithm also identifies the damages, and provides the relative severity coefficients as 0.0354 in (6,5) and 0.0081 in (7,5). For the Localized algorithm, convergence rate is depicted in Fig.…”

Section: B Single-damage Patternmentioning

confidence: 99%

“…For example, the cost of installing 350 sensing channels on the Tsing Ma suspension bridge in Hong Kong is estimated to have exceeded million [5]. Recent development in micro-fabrication and wireless communication technologies enables the replacement of coaxial wires by wireless sensors that are both affordable and easy to deploy [6]- [8]. The sensing and communication capabilities of wireless sensor networks (WSNs) lead to fundamentally new sensing architectures capable of monitoring large spatially distributed phenomena.…”

mentioning

confidence: 99%

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Localized Structural Health Monitoring Using Energy-Efficient Wireless Sensor Networks

et al. 2009

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Abstract-This paper presents a localized information processing approach for long-term, online structural health monitoring (SHM) using wireless sensor networks (WSNs). Based on the embedded AR-ARX method, each sensor independently calculates a statistical damage-sensitive coefficient using the measured acceleration data during each monitoring period. A nonlinear programming formulation is developed to identify damage presence, localize damage position, and quantify damage severity from the damage-sensitive coefficients in the whole sensing field. By limiting each sensor to exchange information among its neighboring sensors only, a localized near-optimal algorithm is proposed to reduce communication costs, thus alleviating the channel interference and prolonging the network lifetime. Simulation results on a steel frame structure prove the effectiveness of the proposed algorithm.Index Terms-Localized information processing, structure health monitoring (SHM), wireless sensor networks (WSNs).

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Section: B Statistical Pattern Recognitionmentioning

confidence: 99%

Section: A Problem Formulationmentioning

confidence: 99%

“…The data has been shown in Fig. 3, with stiffness reduction as 57% in a single column between (6,5) and (7,5). The Threshold algorithm identifies the damages in points (6,5) and (7,5).…”

Section: B Single-damage Patternmentioning

confidence: 99%