Measuring Material Moisture in Fiber Reinforced Polymers by Integrated Sensors

Salas, Mariugenia; Hübner, Martina; Borysov, Mykhailo; Koerdt, Michael; Rennoch, Marcel; Herrmann, Axel S.; Lang, Walter

doi:10.1109/jsen.2018.2815029

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…Most elegant approaches to implement a damage detection are directly integrated monitoring systems inside the component, so that no large external aperture is necessary during maintenance. Approaches of integrating sensors or sensor systems into FRP have been investigated in recent years [3][4][5][6][7][8]. Nevertheless, there are only a few such systems integrated inside FMLs.…”

Section: Introductionmentioning

confidence: 99%

Considerations and Limits of Embedding Sensor Nodes for Structural Health Monitoring into Fiber Metal Laminates

Bornemann

Lang

2022

Sensors

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The objective of this article is to present the results of our investigations concerning the environmental conditions that can be expected during the embedding process into fibre metal laminates and the consequences for a sensor node for structural health monitoring. The idea behind this investigation is to determine for which manufacturing conditions the integration of sensor nodes into the material can be done and to identify limits for this. The sensor nodes consist of commercially available integrated circuits and passive components soldered onto an adhesive-less flexible printed circuit board. They are tested under conditions above their specified limits, to find out if they are still working reliably after experiencing 155 min of 180 ∘C and 7 bar of pressure. Apart from occurring temperature damage, the effect of surrounding fibres potentially pushing away the components under the amount of pressure of the manufacturing process, as well as the potential of shorts due to conductive fibers are investigated and suitable solutions to prevent this are evaluated. One experiment exceeding the typical requirements of a fiber metal laminate embedding process for structural components will be conducted at 250 ∘C for 10 h, in order to determine the limits of embedding electronic sensor nodes. This time and temperature combination is expected to cause irreversible damage to the electronic system. Results show that it is possible to integrate electronics into materials under conditions far above their specifications when precautions are taken but also that there are limits that must not be exceeded during the embedding process.

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Section: Introductionmentioning

confidence: 99%

Considerations and Limits of Embedding Sensor Nodes for Structural Health Monitoring into Fiber Metal Laminates

Bornemann

Lang

2022

Sensors

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“…Therefore, many scholars have made exploratory attempts to control the intelligent aspects of RFID tags, and reconfigurable antennas provide sufficient flexibility and adaptability for the rapidly changing needs of wireless systems and their operating environments. An antenna pattern reconfiguration by controlling PIN diodes was proposed by Strangfeld et al [13] and Salas et al [14] proposed a frequency reconfigurable antenna was proposed by the length of liquid metal in 2018.…”

Section: Introductionmentioning

confidence: 99%

An encoded reconfigurable RFID strain sensor and its information fusion method

Chen¹,

Kang²,

Liu³

et al. 2022

Smart Mater. Struct.

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Chip-based radio frequency identification (RFID) sensor systems have been investigated for structural health monitoring (SHM) applications. However, there are disadvantages to using chips in sensor tags in terms of price, robustness and detecting power. In this paper, we propose a reconfigurable RFID-based chipless tag sensor for strain detection, which detects structural strain by observing the offset of the resonant frequency of the antenna and incorporates an encoding unit for tag identification. To realize the reconfigurability of the antenna, a PIN diode is added to control the antenna, and the frequency reconfigurability of the antenna is realized by controlling the turn on/off of the PIN diodes. The Kalman filter algorithm is used to realize the information fusion of multiple sensors to improve the sensor detection accuracy, and the background noise data enhancement is used to expand the original data samples to fuse the information of the three sensors' data. The relative inaccuracy is roughly 33.5% less after fusion compared to the measurement alone. The antenna was fabricated by an etching process and measured using VNA to verify the accuracy of the antenna simulation results.

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“…Prescinding from resources issues and complexity constraints, an intelligent structure will typically experience changing environmental and operational conditions (EOC) during its life span, such as load, moisture, or temperature. Both for GEW (Douglass and Harley, 2020; Kexel et al, 2021; Moll et al, 2019b; Salas et al, 2018) as well as the EMI technique (Lim et al, 2021; Neuschwander et al, 2019; Wandowski et al, 2017) the presence of EOC can significantly exacerbate SHM-related signal processing. The large diversity in EOC may complicate damage detection.…”

Section: Introductionmentioning

confidence: 99%

Detecting damage in rudder stocks under load using electro-mechanical susceptance: Frequency-warping and semi-supervised approaches

Kexel

Moll

2021

Journal of Intelligent Material Systems and Structures

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Active piezoelectric transducers are successfully deployed in recent years for structural health monitoring using guided elastic waves or electro-mechanical impedance (EMI). In both domains, damage detection can be hampered by operational/environmental conditions and low-power constraints. In both domains, processing can be divided into approaches (i) taking into account baselines of the pristine structure as reference, (ii) ingesting an extensive measurement history for clustering to explore anomalies, (iii) incorporating additional information to label a state. The latter approach requires data from complementary sensors, learning from laboratory/field experiments or knowledge from simulations which may be infeasible for complex structures. Semi-supervised approaches are thus gaining popularity: few initial annotations are needed, because labels emerge through clustering and are subsequently used for state classification. In our work, bending and combined bending/torsion studies on rudder stocks are considered regarding EMI-based damage detection in the presence of load. We discuss the underpinnings of our processing. Then, we follow strategy (i) by introducing frequency warping to derive an improved damage indicator (DI). Finally, in a semi-supervised manner, we develop simple rules which even in presence of varying loads need only two frequency points for reliable damage detection. This sparsity-enforcing low-complexity approach is particularly beneficial in energy-aware SHM scenarios.

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Measuring Material Moisture in Fiber Reinforced Polymers by Integrated Sensors

Cited by 10 publications

References 23 publications

Considerations and Limits of Embedding Sensor Nodes for Structural Health Monitoring into Fiber Metal Laminates

Considerations and Limits of Embedding Sensor Nodes for Structural Health Monitoring into Fiber Metal Laminates

An encoded reconfigurable RFID strain sensor and its information fusion method

Detecting damage in rudder stocks under load using electro-mechanical susceptance: Frequency-warping and semi-supervised approaches

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