Chapter 8 Modal Analysis Using a UAV-Deployable Wireless Sensor Networ k Joud N. Satme, Ryan Yount, Jacob Vaught, Jason Smith, and Austin R. J. Downey Abstrac t In structural health monitoring, wireless sensor networks are favorable for their minimal invasiveness, ease of deployment, and passive monitoring capabilities. Wireless vibration sensor nodes have been implemented successfully for frequency domain analysis in ambient vibration detection. To leverage advances in structural damage quantification techniques, which require modal information, nodes in a wireless sensor network must operate with a near-synchronous clock to enable the collection of the signal phase. The non-deterministic timing nature of wireless systems raises a significant challenge when trying to accurately determine the phase of a signal. In particular, the trigger time delay of the various nodes on the structure cannot be differentiated from a true phase caused by the examined system. This study investigates the reliability and error-handling capabilities of the ShockBurst 2.4 GHz wireless protocol in triggering and data transfer. Building on an open-source UAV-deployable sensor node, mode shapes from a 2-meter test specimen are experimentally determined. An optimization technique that enhances time domain accuracy for non-deterministic wireless triggers is presented. This work quantifies latency and error management effects that contribute to enhancing the modal extraction capabilities of wireless systems in structural health monitoring applications. Keyword s SHM · Sensors · Modal analysis · Vibrations · Dynamic 8.1 Introduction Structural Health Monitoring (SHM) is a Nondestructive Inspection process carried out by measuring the parameters of a given system to infer the current structural state. This process relies on damage identification and quantification algorithms [1]. Furthermore, SHM is used to monitor changes (i.e., damage) in the system through its life cycle to make actionable decisions such as structural repairs. SHM is crucial in extending infrastructures’ operational lifespan and maintaining safety following extreme weather conditions. Its purpose is highly dependent on the system in question. For example, the goal for SHM is drastically different between a railroad bridge and a naval ship. Continuing, SHM for infrastructure primarily assesses changes that take place on a long timescale (i.e., fatigue) while SHM for naval ships is used for various damage types that occur on short and long time scales such as impact, fatigue, and corrosion. While SHM for both structures assesses fatigue damage, the actionable decisions conducted for each structure are different. Vibration-oriented damage detection for structural components is used to evaluate the dynamic and structural property changes as damage indicators. A common vibration-based damage detection technique is modal analysis, where the modes of the structure’s ground truth state are analytically and experimentally determined. These modes are then compared to future states in the structure’s life cycle to quantify differences between each state, and any differences detected signify damage in the structure. Damage detection methods such as acoustic emission analysis are a passive Non-Destructive Testing Techniques (NDTs) that have been successively used on structures such as bridges, tunnels, pipes, and buildings. This method is superior at J. N. Satme · R. Yount · J. Vaught · J. Smith Department of Mechanical Engineering, University of South Carolina, Columbia, SC, USA e-mail: Jsatme@email.sc.edu; RJYOUNT@email.sc.edu; jvaught@sc.edu; JMS32@email.sc.edu A. R. J. Downey ( ) Department of Mechanical Engineering, University of South Carolina, Columbia, SC, USA Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC, USA e-mail: austindowney@sc.edu © The Society for Experimental Mechanics, Inc. 2024 B. J. Dilworth et al. (eds.), Topics in Modal Analysis & Parameter Identification, Volume 9 , Conference Proceedings of the Society for Experimental Mechanics Series, https://doi.org/10.1007/978-3-031-34942-3_8 75
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