Temperature Compensation of SAW Winding Tension Sensor Based on PSO-LSSVM Algorithm
Yang Feng1, Wenbo Liu1, Haoda Yu1
1School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China.
Micromachines
|November 25, 2023
Summary
This study introduces an improved Surface Acoustic Wave (SAW) winding tension sensor. Data fusion and a PSO-LSSVM algorithm significantly enhance measurement accuracy by compensating for temperature errors.
Area of Science:
- Materials Science
- Sensor Technology
- Data Science
Background:
- Traditional winding tension sensors face challenges with measurement accuracy, particularly due to temperature variations.
- Surface Acoustic Wave (SAW) devices offer potential for high-precision sensing applications.
Purpose of the Study:
- To design a high-precision SAW winding tension sensor.
- To improve the measurement accuracy of SAW winding tension sensors using data fusion and advanced algorithms.
- To compensate for temperature-induced errors in SAW sensor measurements.
Main Methods:
- Design of a SAW winding tension sensor utilizing unbalanced split-electrode interdigital transducers (IDTs) and an electrode-overlap envelope.
- Application of Particle Swarm Optimization-Least Squares Support Vector Machine (PSO-LSSVM) algorithm for temperature error compensation.
- Data fusion techniques for enhanced measurement precision.
Main Results:
- The sensitivity temperature coefficient (αs) of the SAW sensor was reduced by an order of magnitude after temperature compensation.
- The PSO-LSSVM model achieved a significantly lower output error (0.50%) compared to the LSSVM model (1.42%).
- Overall accuracy of forecasted results was demonstrated to be 5.95%.
Conclusions:
- The developed SAW winding tension sensor with PSO-LSSVM compensation demonstrates superior measurement accuracy.
- The proposed method offers a novel approach for data analysis and error compensation in SAW winding tension sensors.
- This advancement contributes to more reliable tension monitoring in winding processes.
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