Modular, wireless network platform for monitoring structures

Straser, Erik G.; Kiremidjian, Anne S.; Meng, Tianhua; Redlefsen, L.

doi:10.1016/s0920-5489(99)91996-7

Cited by 123 publications

(157 citation statements)

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“…Many wireless solutions have been proposed for SHM [1,2,4] but virtually all of them consider the wireless connection merely as a convenient replacement for wires that would be expensive to deploy. The stochastic nature of the wireless channel is neither explicitly considered nor exploited, which leads to inefficient protocol and overall system design.…”

Section: Network Architecturementioning

confidence: 99%

“…As an important trade-off in damage detection capability and computational burden is necessary for feasible implementation on the wireless platform, the efficacy of low-order models is demonstrated herein (p=6, a=3, b=3). The reference pool is defined by 300 undamaged data sets generated from independent, random excitations to the system, subsequently fit by the AR model in (1).…”

Section: Objective 2: Damage Detection By Heterogeneous Data Fusionmentioning

confidence: 99%

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Wireless Sensor Networks for Structural Health Monitoring: A Multi-Scale Approach

Kijewski-Correa

Haenggi

Antsaklis

2006

Structures Congress 2006

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Given the present burdens associated with inspection and maintenance of Civil Infrastructure, the development of effective, automated damage diagnosis techniques, including the sensor technologies that support them, has become a major research need. While recent developments in wireless sensor networks have demonstrated their potential to provide continuous structural response data to quantitatively assess structural health, many important issues including network lifetime and stability, damage detection reliability, and trade-offs in model order to balance computational capabilities must be realistically addressed. Only then can wireless embedded sensor networks become a practical tool for Structural Health Monitoring of large, complex Civil Structures. In response to these needs, the concept of a multi-scale wireless sensor network is introduced in this study with a restricted input network activation scheme and the integration of data from a heterogeneous sensor array to improve damage detection for low-order models. The multi-scale network concept introduced here helps to improve power efficiency, minimize packet loss and latency, and eliminate synchronization issues through the use of a decentralized analysis scheme and the activation of sub-networks only in the vicinity of suspected damage, while reducing the required size of the reference pool for the undamaged state. This study introduces the network architecture concept, a strain-driven approach to damage detection and preliminary simulated results. PREVIOUS WORK IN WIRELESS STRUCTURAL HEALTH MONITORINGMost research in these areas of "intelligent" structural assessment and evaluation retain the traditional architecture of a centralized data acquisition hub wired to tens or even hundreds of sensors. As cost effectiveness is a major concern, the installation and maintenance of these cabled systems represent significant concerns, prompting the move toward wireless sensor networks, particularly those with local processing capabilities, thus reducing the amount of data transmitted in power-consuming wireless radio communications. Early work in this area was led by Straser and Kiremidjian [1] and advanced by Lynch et al.'s [2] dual processor format, followed by a later generation lowpower, multi-channel prototype [3]. Another example is the multi-hop WISDEN system [4], which uses the small MICA2 Motes, developed at the University of California at Berkeley [5]. In all of these applications, by reducing transmission from lengthy time histories to a number of key parameters, battery life of the wireless nodes can be extended, while the issues of strict time synchronization and loss intolerance in harsh environmental conditions are marginalized. Building on these prior efforts, the proposed approach offers a multi-scale network topology that is inherently more scaleable and exploits short wired communications for better energy-efficiency and reduced latency. SUMMARY OF KEY FEATURESThough such efforts have certainly made important contributions to the fiel...

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Section: Network Architecturementioning

confidence: 99%

Section: Objective 2: Damage Detection By Heterogeneous Data Fusionmentioning

confidence: 99%

Wireless Sensor Networks for Structural Health Monitoring: A Multi-Scale Approach

Kijewski-Correa

Haenggi

Antsaklis

2006

Structures Congress 2006

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“…However, data generated by wireless structural monitoring sensors are asynchronous as expressed by Equation (1). The value of j is not known and the excitation values x g (t m − j ) are not recorded.…”

Section: Time Synchronization Algorithmmentioning

confidence: 99%

“…Di culties with installation and maintenance of current wired monitoring systems have led to the development of low-cost (less than $1000 per sensing unit) wireless sensors for the health monitoring of civil structures [1][2][3]. Wireless sensors, however, may trigger at di erent times, thus data from sensors may not have the same initial time stamp.…”

Section: Introductionmentioning

confidence: 99%

Algorithms for time synchronization of wireless structural monitoring sensors

Lei

Kiremidjian

Nair

et al. 2005

Earthquake Engng Struct. Dyn.

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SUMMARYDense networks of wireless structural health monitoring systems can e ectively remove the disadvantages associated with current wire-based sparse sensing systems. However, recorded data sets may have relative time-delays due to interference in radio transmission or inherent internal sensor clock errors. For structural system identiÿcation and damage detection purposes, sensor data require that they are time synchronized. The need for time synchronization of sensor data is illustrated through a series of tests on asynchronous data sets. Results from the identiÿcation of structural modal parameters show that frequencies and damping ratios are not in uenced by the asynchronous data; however, the error in identifying structural mode shapes can be signiÿcant. The results from these tests are summarized in Appendix A.The objective of this paper is to present algorithms for measurement data synchronization. Two algorithms are proposed for this purpose. The ÿrst algorithm is applicable when the input signal to a structure can be measured. The time-delay between an output measurement and the input is identiÿed based on an ARX (auto-regressive model with exogenous input) model for the input-output pair recordings. The second algorithm can be used for a structure subject to ambient excitation, where the excitation cannot be measured. An ARMAV (auto-regressive moving average vector) model is constructed from two output signals and the time-delay between them is evaluated. The proposed algorithms are veriÿed with simulation data and recorded seismic response data from multi-story buildings. The in uence of noise on the time-delay estimates is also assessed.

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“…Wireless sensor network (WSN) has shown potential for structural health monitoring (SHM) (Straser and Kiremidjian, 1998;Spencer et al, 2004;Lynch, 2007), because its installation and maintenance are easier and less expensive than a wired network. In many structural applications of WSNs, however, it is not enough to observe only the structural state.…”

Section: Introductionmentioning

confidence: 99%

Active structures integrated with wireless sensor and actuator networks: a bio-inspired control framework

Yang

Shen

Luo

2016

J. Zhejiang Univ. Sci. A

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Abstract:One of the main problems in controlling the shape of active structures (AS) is to determine the actuations that drive the structure from the current state to the target state. Model-based methods such as stochastic search require a known type of load and relatively long computational time, which limits the practical use of AS in civil engineering. Moreover, additive errors may be produced because of the discrepancy between analytic models and real structures. To overcome these limitations, this paper presents a compound system called WAS, which combines AS with a wireless sensor and actuator network (WSAN). A bio-inspired control framework imitating the activity of the nervous systems of animals is proposed for WAS. A typical example is tested for verification. In the example, a triangular tensegrity prism that aims to maintain its original height is integrated with a WSAN that consists of a central controller, three actuators, and three sensors. The result demonstrates the feasibility of the proposed concept and control framework in cases of unknown loads that include different types, distributions, magnitudes, and directions. The proposed control framework can also act as a supplementary means to improve the efficiency and accuracy of control frameworks based on a common stochastic search.

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Modular, wireless network platform for monitoring structures

Cited by 123 publications

References 0 publications

Wireless Sensor Networks for Structural Health Monitoring: A Multi-Scale Approach

Wireless Sensor Networks for Structural Health Monitoring: A Multi-Scale Approach

Algorithms for time synchronization of wireless structural monitoring sensors

Active structures integrated with wireless sensor and actuator networks: a bio-inspired control framework

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