Assessment of Signal Transmission using Microwave Sensors for Health Monitoring Applications

2018 ◽  
Author(s):  
Syaiful Redzwan ◽  
Jacob Velander ◽  
Noor Badariah Asan ◽  
Mauricio D Perez ◽  
Robin Augustine
Keyword(s):  
Health Monitoring ◽  
Signal Transmission ◽  
Monitoring Applications ◽  
Microwave Sensors

scholarly journals Analysis of Thickness Variation in Biological Tissues Using Microwave Sensors for Health Monitoring Applications

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 156033-156043
Author(s):  
Syaiful Redzwan Mohd Shah ◽  
Noor Badariah Asan ◽  
Jacob Velander ◽  
Javad Ebrahimizadeh ◽  
Mauricio D. Perez ◽  
...  
Keyword(s):  
Health Monitoring ◽  
Biological Tissues ◽  
Thickness Variation ◽  
Monitoring Applications ◽  
Microwave Sensors

Continuous missing data imputation with incomplete dataset by generative adversarial networks–based unsupervised learning for long-term bridge health monitoring

2021 ◽  
pp. 147592172110219
Author(s):  
Huachen Jiang ◽  
Chunfeng Wan ◽  
Kang Yang ◽  
Youliang Ding ◽  
Songtao Xue
Keyword(s):  
Missing Data ◽  
Health Monitoring ◽  
Signal Transmission ◽  
Imputation Accuracy ◽  
Generative Adversarial Networks ◽  
Data Imputation ◽  
Sensor Failure ◽  
Generative Adversarial Network ◽  
Missing Data Imputation ◽  
Adversarial Network

Wireless sensors are the key components of structural health monitoring systems. During the signal transmission, sensor failure is inevitable, among which, data loss is the most common type. Missing data problem poses a huge challenge to the consequent damage detection and condition assessment, and therefore, great importance should be attached. Conventional missing data imputation basically adopts the correlation-based method, especially for strain monitoring data. However, such methods often require delicate model selection, and the correlations for vehicle-induced strains are much harder to be captured compared with temperature-induced strains. In this article, a novel data-driven generative adversarial network (GAN) for imputing missing strain response is proposed. As opposed to traditional ways where correlations for inter-strains are explicitly modeled, the proposed method directly imputes the missing data considering the spatial–temporal relationships with other strain sensors based on the remaining observed data. Furthermore, the intact and complete dataset is not even necessary during the training process, which shows another great superiority over the model-based imputation method. The proposed method is implemented and verified on a real concrete bridge. In order to demonstrate the applicability and robustness of the GAN, imputation for single and multiple sensors is studied. Results show the proposed method provides an excellent performance of imputation accuracy and efficiency.

Development of PZT fiber rosette to locate acoustic source

2021 ◽  
pp. 1045389X2199192
Author(s):  
Bao Chi Ha ◽  
Kevin Gilbert ◽  
Gang Wang
Keyword(s):  
Lead Zirconate Titanate ◽  
Health Monitoring ◽  
Lamb Wave ◽  
Lead Zirconate ◽  
Acoustic Source ◽  
Mechanical Coupling ◽  
Monitoring Applications ◽  
Commercial Off The Shelf ◽  
Lamb Wave Propagation ◽  
Strain Direction

Because of their electro-mechanical coupling property, Lead-Zirconate-Titanate (PZT) materials have been widely used for ultrasonic wave sensing and actuation in structural health monitoring applications. In this paper, a PZT rosette concept is proposed to conduct Lamb wave-based damage detection in panel-like structures by exploring its best directional sensing capability. First, a directivity study was conducted to investigate sensing of flexural Lamb wave propagation using a PZT fiber having d33 effects. Then, commercial off-the-shelf PZT fibers were polarized in-house in order to construct the PZT rosette configuration, in which three PZT fibers are oriented at 0°, 45°, 90°, respectively. Since Lamb wave responses are directly related to measured PZT fiber voltage signals, a simple interrogation scheme was developed to calculate principal strain direction in order to locate an acoustic source. Comprehensive tests were conducted to evaluate the performance of the proposed PZT rosette using an aluminum plate. It is shown that the PZT rosette is able to sense Lamb wave responses and accurately locate an acoustic source. We expect to further evaluate the PZT rosette performance when damages are introduced.

scholarly journals Hybrid Carbon Microfibers-Graphite Fillers for Piezoresistive Cementitious Composites

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 518
Author(s):  
Hasan Borke Birgin ◽  
Antonella D’Alessandro ◽  
Simon Laflamme ◽  
Filippo Ubertini
Keyword(s):  
Percolation Threshold ◽  
Health Monitoring ◽  
Cementitious Materials ◽  
Strain Sensing ◽  
Composite Matrix ◽  
Hybrid Fillers ◽  
Monitoring Applications ◽  
Conductive Fillers ◽  
Carbon Microfibers ◽  
Hybrid Carbon

Multifunctional structural materials are very promising in the field of engineering. Particularly, their strain sensing ability draws much attention for structural health monitoring applications. Generally, strain sensing materials are produced by adding a certain amount of conductive fillers, around the so-called “percolation threshold”, to the cement or composite matrix. Recently, graphite has been found to be a suitable filler for strain sensing. However, graphite requires high amounts of doping to reach percolation threshold. In order to decrease the amount of inclusions, this paper proposes cementitious materials doped with new hybrid carbon inclusions, i.e., graphite and carbon microfibers. Carbon microfibers having higher aspect ratio than graphite accelerate the percolation threshold of the graphite particles without incurring into dispersion issues. The resistivity and strain sensitivity of different fibers’ compositions are investigated. The electromechanical tests reveal that, when combined, carbon microfibers and graphite hybrid fillers reach to percolation faster and exhibit higher gauge factors and enhanced linearity.

scholarly journals Energy Efficiency of Fog Computing Health Monitoring Applications

2018 ◽  
Author(s):  
Ida Syafiza M. Isa ◽  
Mohamed O. I. Musa ◽  
Taisir E. H. El-Gorashi ◽  
Ahmed Q. Lawey ◽  
Jaafar M. H. Elmirghani
Keyword(s):  
Energy Efficiency ◽  
Health Monitoring ◽  
Fog Computing ◽  
Monitoring Applications
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