RFID Antenna Sensor for Quantitatively Monitoring Surface Crack Growth

Wang, Chao; Wang, Cong; Wan, Guo Chun; Tong, Mei Song; Guan, Shuai; Xie, Liyu

doi:10.1109/compem.2019.8778938

Cited by 7 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the monitoring of concrete cracks, research on RFID technology for crack monitoring and detection in metal materials has also been extensive [39][40][41][42][43]. Li et al [44] proposed a structural health monitoring sensor utilizing RFID technology, which incorporated three RFID tags for identification, as well as a circular patch antenna for crack detection.…”

Section: Rfid-based Crack Monitoring Applications For Metal Materialsmentioning

confidence: 99%

A Concise State-of-the-Art Review of Crack Monitoring Enabled by RFID Technology

Ran,

Wang,

Wang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Cracking is an important factor affecting the performance and life of large structures. In order to maximize personal safety and reduce costs, it is highly necessary to carry out research on crack monitoring technology. Sensors based on Radio Frequency Identification (RFID) antennas have the advantages of wireless and low cost, which makes them highly competitive in the field of structure health monitoring (SHM). Thus, this study systematically summarizes the research progress of crack monitoring based on RFID technology in recent years. Firstly, this study introduces the causes of cracks and the traditional monitoring methods. Further, this study summarizes several main RFID-based crack monitoring and detection methods, including crack monitoring based on chipless RFID technology, passive RFID technology, and ultra-high-frequency (UHF) RFID technology, including the implementation methods, as well as the advantages and disadvantages of those technologies. In addition, for RFID-based crack monitoring applications, the two most commonly used materials are concrete materials and metal materials, which are also illustrated in detail. In general, this study can provide technical support and a theoretical basis for crack monitoring and detection to ensure the safety of engineering structures.

show abstract

Section: Rfid-based Crack Monitoring Applications For Metal Materialsmentioning

confidence: 99%

A Concise State-of-the-Art Review of Crack Monitoring Enabled by RFID Technology

Ran,

Wang,

Wang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…These difficulties can be overcome by using sensor-based tag antennas operating in ultrahighfrequency (UHF) regions. The formation of cracks or deformation in the structure is detected by continuously monitoring the resonant frequency of the antenna sensor [5]. An open-ended waveguide is used for surface crack detection.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Detection of Pipe Line Cracks Using Ultra Wide Band Antenna with Machine Learning Algorithm

Venkatesan

Kalimuthu

2023

ACES Journal

View full text Add to dashboard Cite

In this article, an Ultra-Wide Band (UWB) antenna for the pipeline crack detection process is proposed. A UWB antenna has been designed with the dimension of 32 x 32 mm2 and it resonates from 3 GHz to 10.8 GHz. The designed antenna produces a peak gain of 4.36 dB. A pair of UWB antennas are employed in various pipeline scenarios and the received pulse from antenna 1 to antenna 2 is used for further processing and detection of pipeline cracks. Through the suitable machine learning data classifier algorithm the dimension of the crack has been detected. The various features such as mean, standard deviation (σ), mean average deviation (mad), skewness, and kurtosis have been extracted from the received pulse. Then the three different machine learning algorithms namely Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Naïve Bayse (NB) were trained and tested using extracted features, and the dimension of the void has been identified. Out of these three machine learning algorithms, kNN provides better accuracy and precision. It predicts the small cracks with 100% accuracy having a dimension as small as 1 mm width.

show abstract

Design and Analysis of Quad Element UWB MIMO Antenna with Mutual Coupling Reduction Techniques

Venkatesan

Kalimuthu

Kumar

2023

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

RFID Antenna Sensor for Quantitatively Monitoring Surface Crack Growth

Cited by 7 publications

References 8 publications

A Concise State-of-the-Art Review of Crack Monitoring Enabled by RFID Technology

A Concise State-of-the-Art Review of Crack Monitoring Enabled by RFID Technology

Non-Destructive Detection of Pipe Line Cracks Using Ultra Wide Band Antenna with Machine Learning Algorithm

Design and Analysis of Quad Element UWB MIMO Antenna with Mutual Coupling Reduction Techniques

Contact Info

Product

Resources

About