Machine Learning-Based Structural Health Monitoring Using RFID for Harsh Environmental Conditions
Abstract
:1. Introduction
2. Proposed Methodology
2.1. RFID Base Material Identification and Crack Sensing
2.2. Machine Learning-Aided Material Identification
2.3. Material Sample Preparation
2.4. Crack Information
2.5. Data Preparation and Accuracy Equation
3. Experiment Validation
3.1. Experiment Setup
3.2. COTS RFID Tags for Validation
3.3. Validation Results
3.4. Validation of Crack Sensing Capability of RFID
3.5. Discussion
4. Conclusions and Future Works
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviation
Acronyms | Definition |
ANN | Artificial neural network |
ASK | Amplitude shift keying |
BP | Backscattered power |
COTS | Commercial off-the-shelf |
EF | Electric field |
EMAT | Electromagnetic acoustic transducer |
FR-4 | Flame retardant-4 |
FS | Fuzzy logic-based system |
HF | High frequency |
IAEA | International Atomic Energy Agency |
IC | Integrated circuit |
IoT | Internet of Things |
LF | Low frequency |
MFL | Magnetic flux leakage |
MHz | Megahertz |
ML | Machine learning |
MLNN | Multi-layer neural network |
MRC | Magnetic resonance coupling |
NDT | Non-destructive testing |
NDT&E | Non-destructive testing and evaluation |
PE | Processing element |
PH | Phase |
POM | Polyoxymethylene |
POCO | Post Operation Clean Out |
PTFE | Polytetrafluoroethylene |
PVC | Polyvinylchloride |
ReLU | Rectified Linear Unit |
RFID | Radio frequency identification |
SHM | Structural Health Monitoring |
SVM | Support vector machines |
TP | Transmitted power |
UHF | Ultra-high frequency |
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Material | Relative Permittivity | Dielectric Loss Tangent |
---|---|---|
Cardboard | 2.57 | 0.0717 |
FR-4 | 4.87 | 0.0141 |
Glass | 7.11 | 0.0098 |
Polyoxymethylene (POM) | 2.96 | 0.0450 |
Polytetrafluoroethylene (PTFE) | 2.05 | 0.0002 |
Polyvinylchloride (PVC) | 3.00 | 0.0079 |
Rubber | 6.73 | 0.0247 |
Transponder | Size (mm) | Read Range (m) | Operation Band (MHz) |
---|---|---|---|
a | 49.8 × 14 × 0.4 | 3–3.6 | 865–928 |
b | 25 × 9 × 3.7 | 1.8 | 902–928 |
c | Round 34 × 34 × 6 | NA | 902–928 |
d | 80 × 16 × 0.1 | NA | NA |
e | 116 × 22 × 0.9 | NA | 800–1000 |
Method | Tree | Naïve Bayes | 2-Layer MLNN | 3-Layer MLNN |
---|---|---|---|---|
Accuracy | 64.4% | 23.4% | 77.9% | 78.9% |
Time | 1.1821 s | 19.765 s | 102.32 s | 149.19 s |
Crack Parameter | Tree | Naïve Bayes | 2-Layer MLNN | 3-Layer MLNN |
---|---|---|---|---|
Length | 82.1%, 1.4737 s | 66.4%, 14.027 s | 90.8%, 73.589 s | 91.3%, 111.05 s |
Width | 88.2%, 1.2931 s | 65.2%, 8.3985 s | 94.2%, 70.958 s | 94.2%, 105.94 s |
Depth | 79.8%, 1.5238 s | 48.0%, 14.052 s | 90.9%, 73.596 s | 91.4%, 107.57 s |
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Zhao, A.; Sunny, A.I.; Li, L.; Wang, T. Machine Learning-Based Structural Health Monitoring Using RFID for Harsh Environmental Conditions. Electronics 2022, 11, 1740. https://doi.org/10.3390/electronics11111740
Zhao A, Sunny AI, Li L, Wang T. Machine Learning-Based Structural Health Monitoring Using RFID for Harsh Environmental Conditions. Electronics. 2022; 11(11):1740. https://doi.org/10.3390/electronics11111740
Chicago/Turabian StyleZhao, Aobo, Ali Imam Sunny, Li Li, and Tengjiao Wang. 2022. "Machine Learning-Based Structural Health Monitoring Using RFID for Harsh Environmental Conditions" Electronics 11, no. 11: 1740. https://doi.org/10.3390/electronics11111740