Wireless Measurement System for Structural Health Monitoring With High Time-Synchronization Accuracy

Araújo, Álvaro; García‐Palacios, Jaime H.; Blesa, Javier; Tirado, Francisco; Romero, Elena; Samartín, Avelino; Nieto–Taladriz, Octavio

doi:10.1109/tim.2011.2170889

Cited by 99 publications

(67 citation statements)

References 12 publications

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“…Only a few justify the use of WSNs, where sensors are often required to transmit data to a central server (or a sink) [Ceriotti et al 2009;Araujo et al 2012;Hackmann et al 2012;Li et al 2013;Linderman et al 2010;Farrar and Worden 2012]. By using either single-hop or multihop communication techniques, data transmission over a large structure is extremely difficult and costly.…”

Section: Introductionmentioning

confidence: 99%

Sensor Placement with Multiple Objectives for Structural Health Monitoring

Bhuiyan

Wang

Cao

et al. 2014

ACM Trans. Sen. Netw.

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Structural health monitoring (SHM) refers to the process of implementing a damage detection and characterization strategy for engineering structures. Its objective is to monitor the integrity of structures and detect and pinpoint the locations of possible damages. Although wired network systems still dominate in SHM applications, it is commonly believed that wireless sensor network (WSN) systems will be deployed for SHM in the near future, due to their intrinsic advantages. However, the constraints (e.g., communication, fault tolerance, energy) of WSNs must be considered before their deployment on structures. In this article, we study the methodology of sensor placement optimization for WSN-based SHM. Sensor placement plays a vital role in SHM applications, where sensor nodes are placed on critical locations that are of civil/structural engineering importance. We design a three-phase sensor placement approach, named TPSP, aiming to achieve the following objectives: finding a high-quality placement for a given set of sensors that satisfies the engineering requirements, ensuring communication efficiency and reliability and low placement complexity, and reducing the probability of failures in a WSN. Along with the sensor placement, we enable sensor nodes to develop "connectivity trees" in such a way that maintaining structural health state and network connectivity, for example, in case of a sensor fault, can be done in a distributed manner. The trees are constructed once (unlike dynamic clusters or trees) and do not incur additional communication costs for the WSN. We optimize the performance of TPSP by considering multiple objectives: low communication cost, fault tolerance, and lifetime prolongation. We validate the effectiveness and performance of TPSP through both simulations using real datasets and a proof-of-concept system on a physical structure.

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Section: Introductionmentioning

confidence: 99%

Sensor Placement with Multiple Objectives for Structural Health Monitoring

Bhuiyan

Wang

Cao

et al. 2014

ACM Trans. Sen. Netw.

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“…Another WSN mote specialized for SHM applications is proposed by [Araujo2012]. It employs two MCUs to separate the controlling of the sensor and the radio interface.…”

Section: Mcu-based Motesmentioning

confidence: 99%

A heterogeneous system architecture for low-power wireless sensor nodes in compute-intensive distributed applications

Engel

Koch

Siebel

2015

2015 IEEE 40th Local Computer Networks Conference Workshops (LCN Workshops)

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Die Veröffentlichung steht unter folgender Creative Commons Lizenz: Namensnennung -Keine kommerzielle Nutzung -Keine Bearbeitung 4.0 International https://creativecommons.org/licenses/by-nc-nd/4.0 Erklärung zur DissertationHiermit versichere ich, die vorliegende Dissertation ohne Hilfe Dritter nur mit den angegebenen Quellen und Hilfsmitteln angefertigt zu haben. Alle Stellen, die aus Quellen entnommen wurden, sind als solche kenntlich gemacht. Diese Arbeit hat in gleicher oder ähnlicher Form noch keiner Prüfungsbehörde vorgelegen. Darmstadt, den 22.11.2016 (Andreas Engel) AbstractWireless Sensor Networks (WSNs) combine embedded sensing and processing capabilities with a wireless communication infrastructure, thus supporting distributed monitoring applications. WSNs have been investigated for more than three decades, and recent social and industrial developments such as home automation, or the Internet of Things, have increased the commercial relevance of this key technology. The communication bandwidth of the sensor nodes is limited by the transportation media and the restricted energy budget of the nodes. To still keep up with the ever increasing sensor count and sampling rates, the basic data acquisition and collection capabilities of WSNs have been extended with decentralized smart feature extraction and data aggregation algorithms. Energy-efficient processing elements are thus required to meet the ever-growing compute demands of the WSN motes within the available energy budget.The Hardware-Accelerated Low Power Mote (HaLoMote) is proposed and evaluated in this thesis to address the requirements of compute-intensive WSN applications. It is a heterogeneous system architecture, that combines a Field Programmable Gate Array (FPGA) for hardware-accelerated data aggregation with an IEEE 802.15.4 based Radio Frequency System-on-Chip for the network management and the top-level control of the applications. To properly support Dynamic Power Management (DPM) on the HaLoMote, a Microsemi IGLOO FPGA with a non-volatile configuration storage was chosen for a prototype implementation, called Hardware-Accelerated Low Energy Wireless Embedded Sensor Node (HaLOEWEn). As for every multi-processor architecture, the inter-processor communication and coordination strongly influences the efficiency of the HaLoMote. Therefore, a generic communication framework is proposed in this thesis. It is tightly coupled with the DPM strategy of the HaLoMote, that supports fast transitions between active and idle modes. Low-power sleep periods can thus be scheduled within every sampling cycle, even for sampling rates of hundreds of hertz.In addition to the development of the heterogeneous system architecture, this thesis focuses on the energy consumption trade-off between wireless data transmission and insensor data aggregation. The HaLOEWEn is compared with typical software processors in terms of runtime and energy efficiency in the context of three monitoring applications. The building blocks of these applications comprise hardware-acce...

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“…This creates the need for synchronization of data acquisition systems. Currently, monitoring systems consisting of multiple wireless nodes are being developed [2,10]. Each of the nodes has its own data acquisition, and-to date-synchronization of the nodes remains a challenge.…”

Section: Introductionmentioning

confidence: 99%

Synchronization of Data Acquisition Systems for the Purpose of Structural Health Monitoring

Maes¹,

Reynders²,

Rezayat³

et al. 2017

Proceedings of the 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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Abstract. This paper presents a technique for offline time synchronization of data acquisition systems for linear structures with proportional damping. The technique can be applied when direct synchronization of data acquisition systems is impossible or not sufficiently accurate, for example when multiple measurement nodes with different clock signals are embedded in a health monitoring network. The synchronization is based on the acquired dynamic response of the structure only, and does not require the acquisition of a shared sensor signal or a trigger signal. The time delay is identified from the spurious phase shift of the mode shape components that are obtained from system identification. A demonstration for a laboratory experiment on a cantilever steel beam shows that the proposed methodology can be used for accurate time synchronization, resulting in a significant improvement of the accuracy of the identified mode shapes.

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Wireless Measurement System for Structural Health Monitoring With High Time-Synchronization Accuracy

Cited by 99 publications

References 12 publications

Sensor Placement with Multiple Objectives for Structural Health Monitoring

Sensor Placement with Multiple Objectives for Structural Health Monitoring

A heterogeneous system architecture for low-power wireless sensor nodes in compute-intensive distributed applications

Synchronization of Data Acquisition Systems for the Purpose of Structural Health Monitoring

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