On‐board data synchronization in wireless structural health monitoring systems based on phase locking. Issue 11 (23rd August 2018)

• Record Type: Journal Article
• Authors: Dragos, Kosmas; Theiler, Michael; Magalhães, Filipe; Moutinho, Carlos; Smarsly, Kay
• Is Part Of: Structural control and health monitoring. Volume 25:Issue 11(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-08-23
• Subjects: embedded computing -- operational modal analysis -- structural health monitoring -- synchronization -- wireless sensor networks; Structural engineering -- Periodicals; Structural control (Engineering) -- Periodicals; Automatic data collection systems -- Periodicals; Detectors -- Periodicals; 624.17
• DOI: 10.1002/stc.2248
• Languages: English
• ISSNs: 1545-2255

Abstract

Summary: Wireless sensor networks are prone to synchronization discrepancies, due to the lack of intrinsic global clock management from a centralized server. In wireless structural health monitoring (SHM) systems, synchronization discrepancies may lead to erroneous estimations of structural parameters of monitored structures. To avoid errors in the estimations of structural parameters, structural response data sets collected from a structure must be synchronized. Synchronization between structural response data sets can be achieved through offline processing. However, in wireless SHM systems, offline processing requires wireless communication of entire structural response data sets, which has been proven detrimental to the power autonomy of wireless sensor nodes. This paper presents an embedded synchronization algorithm for wireless SHM systems. The embedded synchronization algorithm functions as a module added to embedded algorithms performing peak picking, which is part of operational modal analysis, for ensuring accurate outcomes. The embedded synchronization algorithm enables wireless SHM systems to synchronize structural response data sets on board using the embedded computing capabilities of wireless sensor nodes. The synchronization is achieved by imposing the expected relationship between the phase angles of Fourier spectra of acceleration response data sets at peaks corresponding to vibration modes. Time lags are autonomously estimated by the wireless sensor nodes through collaborative analysis of the phase angle relationship between acceleration response data sets collected by different sensor nodes. The embedded synchronization algorithm is implemented into a prototype wireless SHM system with embedded peak picking algorithms and validated by laboratory tests and by ambient vibration tests on a pedestrian bridge.