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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.

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Summary
This summary is machine-generated.

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.

Keywords:
PSO-LSSVM algorithmsurface acoustic wave (SAW)temperature compensationwinding tension sensor

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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.