Issues in structural health monitoring employing smart sensors

Nagayama, Tomonori; Sim, Sung‐Han; Miyamori, Yasunori; Spencer, Billie F.

doi:10.12989/sss.2007.3.3.299

Cited by 136 publications

(110 citation statements)

References 7 publications

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“…Among the middleware services are data aggregation, reliable communication, and synchronized sensing (Nagayama et al 2007). These middleware services are implemented on the Imote2 running TinyOS, while ideas behind these services are generally applicable to other smart sensor platforms.…”

Section: Middleware Servicesmentioning

confidence: 99%

Structural health monitoring using smart sensors

Nagayama¹,

Spencer²,

Rice³

2008

World Forum on Smart Materials and Smart Structures Technology

154

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Industrialized nations have a huge investment in the pervasive civil infrastructure on which our lives rely. To properly manage this infrastructure, its condition or serviceability should be reliably assessed. For condition or serviceability assessment, Structural Health Monitoring (SHM) has been considered to provide information on the current state of structures by measuring structural vibration responses and other physical phenomena and conditions. Civil infrastructure is typically large-scale, exhibiting a wide variety of complex behavior; estimation of a structure's state is a challenging task. While SHM has been and still is intensively researched, further efforts are required to provide efficient and effective management of civil infrastructure. Efforts toward realization of SHM systems using smart sensors, however, have not resulted in full-fledged applications. All efforts to date have encountered difficulties originating from limited resources on smart sensors (e.g., small memory size, small communication throughput, limited speed of the CPU, etc.). To realize an SHM system employing smart sensors, this system needs to be designed considering both the characteristics of the smart sensor and the structures to be monitored.This research addresses issues in smart sensor usages in SHM applications and realizes, for the first time, a scalable and extensible SHM system using smart sensors. The iv architecture of the proposed SHM is first presented. The Intel Imote2 equipped with an accelerometer sensor board is chosen as the smart sensor platform to demonstrate the efficacy of this architecture. Middleware services such as model-based data aggregation, reliable communication, and synchronized sensing are developed. SHM Algorithms identified as promising for the usage on smart sensor systems are extended to improve practicability and implemented on Imote2s. Careful attention has been paid to integrating these software components so that the system possesses identified desirable features.The damage detection capability and autonomous operation of the developed system are then experimentally verified. The SHM system consisting of ten Imote2s are installed on a scale-model truss. The SHM system monitors the truss in a distributed manner to localize simulated damage.In summary, this thesis proposes and realizes a scalable and autonomous SHM system using smart sensors. The system is experimentally verified to be effective for damage

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Section: Middleware Servicesmentioning

confidence: 99%

Structural health monitoring using smart sensors

Nagayama¹,

Spencer²,

Rice³

2008

World Forum on Smart Materials and Smart Structures Technology

154

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“…It is worth noting that this requirement can be easily met in the traditional wired sensing system but not in case of WSNs with inherent synchronization errors. Nagayama et al (2007) noted substantial effects of initial DSE on modal phases detected from simulation model by one parametric OMA method, whereas Krishnamurthy et al (2008) observed considerable influence of initial DSE on mode shape magnitudes estimated by FDD in an experiment. Yan and Dyke (2010) confirmed effects of DSE on mode shapes in both simulation and experimental studies.…”

Section: Major Uncertainties Of Shm-oriented Wsnsmentioning

confidence: 96%

“…However, from a perspective of SHM applications, two major and distinct WSN uncertainties that can directly degrade data quality are data loss and data synchronization error (Nagayama et al, 2007). Brief review and discussion regarding these two factors are presented below.…”

Section: Major Uncertainties Of Shm-oriented Wsnsmentioning

confidence: 99%

“…Data loss has been seen as a serious problem for the generic WSN platform and resulted from unreliable wireless communications between sensor nodes (Nagayama et al, 2007). In SHM-oriented WSNs, reliable communication protocol based on selective negative acknowledgement (NACK) approach have been developed in middleware services so that lost data packets can be resent (Nagayama et al, 2009).…”

Section: Major Uncertainties Of Shm-oriented Wsnsmentioning

confidence: 99%

See 1 more Smart Citation

Effects of Wireless Sensor Network Uncertainties on Output-Only Modal Analysis Employing Merged Data of Multiple Tests

Nguyễn

Chan

Thambiratnam

2014

Advances in Structural Engineering

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The use of Wireless Sensor Networks (WSNs) for Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data synchronization error and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research verifying the applicability of those WSNs with respect to demanding SHM applications like modal analysis and damage identification. This paper first presents a brief review of the most inherent uncertainties of the SHM-oriented WSN platforms and then investigates their effects on outcomes and performance of the most robust Output-only Modal Analysis (OMA) techniques when employing merged data from multiple tests. The two OMA families selected for this investigation are Frequency Domain Decomposition (FDD) and Data-driven Stochastic Subspace Identification (SSI-data) due to the fact that they both have been widely applied in the past decade. Experimental accelerations collected by a wired sensory system on a large-scale laboratory bridge model are initially used as clean data before being contaminated by different data pollutants in sequential manner to simulate practical SHM-oriented WSN uncertainties. The results of this study show the robustness of FDD and the precautions needed for SSI-data family when dealing with SHM-WSN uncertainties. Finally, the use of the measurement channel projection for the time-domain OMA techniques and the preferred combination of the OMA techniques to cope with the SHM-WSN uncertainties is recommended.

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“…This eliminates the need for costly and complex wiring between systems, and also provides flexibility of mesh networking algorithms to transport information from node to node. This allows nodes to be deployed in almost any location and offers flexibility of monitoring a large number of structures, which greatly increases the possibility of using appropriate structural health monitoring solutions [3][4][5]. The wireless sensor network has been developed to address limitations of the existing structural health monitoring techniques, which rely on either periodic visual inspections or expensive wired data acquisition systems.…”

Section: Wireless Sensor Network -System Overviewmentioning

confidence: 99%

Bežični sustav mjerenja relativnih deformacija za ocjenu stanja infrastrukturnih građevina

Lakušić¹

2016

JCE

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Wireless sensors and sensor networks are emerging as substitutes for traditional structural monitoring systems. Their benefit lies in a lower cost of installation because extensive wiring is no longer required between sensors and the data acquisition system. Studies carried out to evaluate performance of wireless strain measurement units are described in this paper. An example is given of a wireless system used for measuring behaviour of a railway bridge, and comparison with traditional systems is made. Key words:Wireless sensor networks, strain, LabVIEW, wireless nodes, wireless gateways Stručni rad Arun Sundaram B., Ravisankar K., Senthil R., Parivallal S. Bežični sustav mjerenja relativnih deformacija za ocjenu stanja infrastrukturnih građevinaBežični senzori i senzorske mreže postaju zamjena za tradicionalne sustave za praćenje ponašanja konstrukcija. Njihova je prednost u nižoj cijeni ugradnje jer nema potrebe za polaganjem dugih kabela između senzora i sustava za prikupljanje podataka. U radu se opisuju istraživanja provedena u svrhu ocjenjivanja učinkovitosti bežičnih senzora za mjerenje relativnih deformacija. Dan je primjer primjene bežičnog senzorskog sustava pri mjerenju ponašanja konstrukcije željezničkog mosta te je napravljena usporedba s primjenom klasičnih sustava.

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Issues in structural health monitoring employing smart sensors

Cited by 136 publications

References 7 publications

Structural health monitoring using smart sensors

Structural health monitoring using smart sensors

Effects of Wireless Sensor Network Uncertainties on Output-Only Modal Analysis Employing Merged Data of Multiple Tests

Bežični sustav mjerenja relativnih deformacija za ocjenu stanja infrastrukturnih građevina

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