Fiber metal laminate-embedded, wireless MEMS vibrometer for structural health monitoring using guided ultrasonic waves
Source abstract
Abstract
Abstract This paper describes and evaluates the embedding of sensors and electronic sensor nodes into fiber metal laminate (FML) plates to achieve material-integrated, guided ultrasonic wave based structural health monitoring for hybrid materials. It evaluates how embedded electronics can enhance the process of sensor data acquisition and at the same time critically investigates the drawbacks that accompany the embedding approach regarding the influence on the received signal. A FML specimen with single sensors in one half of the plate and sensors with attached electronic sensor nodes for wireless readout in the other half is manufactured, introducing the detailed embedding process for such systems. Ultrasonic through-thickness scans of the manufactured plate are presented and analyzed to assess the achieved embedding quality. Together with electric sensor signals from both, wireless and wirebound micro-electromechanical system vibrometers and data from a scanning laser Doppler vibrometer (SLDV) the influence of material-integrated components on the wave propagation around the locations of integration is discussed. Further, the signals of wirebound sensors are successfully correlated with measurements performed using the SLDV and directly compared to data provided by wirelessly readout sensor nodes having the same type of sensor attached. This work shows how reflections occurring due to a material integration of components influence the recorded sensor data. At the same time, it is discussed how, for baseline-based damage detection methods, the influence of this is assumed to be a minor problem, and proof for advantages provided by the integration of complete sensor systems directly into the host material is provided.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Fiber Optic Sensors · Ultrasonics and Acoustic Wave Propagation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (69)
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