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Novel Information-Driven Smoothing Spline Linearization Method for High-Precision Displacement Sensors Based on

Wen-Hao Zhang1, Lin Dai1, Wang Chen1

  • 1State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University, Hangzhou 310058, China.

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Summary

A new smoothing spline linearization method accurately corrects sensor nonlinearity, significantly improving displacement measurement accuracy. This noise-resistant approach enhances precision for high-accuracy displacement sensors, outperforming traditional models.

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Area of Science:

  • Metrology and Measurement Science
  • Sensor Technology
  • Data Analysis and Modeling

Background:

  • Accurate physical displacement retrieval from sensor signals requires noise-resistant linearization models.
  • True sensor nonlinearity must be revealed for high-precision measurements.
  • Existing polynomial and spline methods have limitations in low noise-to-range ratio scenarios.

Purpose of the Study:

  • To propose a novel information-driven smoothing spline linearization method for high-precision displacement sensors.
  • To integrate information criteria for enhanced linearization performance.
  • To evaluate the proposed method against traditional techniques.

Main Methods:

  • Developed an information-driven smoothing spline linearization technique.
  • Integrated one novel and three standard information criteria into the spline model.
  • Employed theoretical analysis and Monte Carlo simulations for evaluation.
  • Conducted validation experiments on chromatic confocal and laser triangulation displacement sensors.

Main Results:

  • The proposed method significantly outperforms traditional polynomial and spline linearization methods.
  • Integration of a modified Akaike Information Criterion demonstrated superior performance.
  • Residual nonlinearity was improved by over 50% compared to standard polynomial models.
  • Achieved residual nonlinearities as low as ±0.0311% F.S. and ±0.0047% F.S. for tested sensors.

Conclusions:

  • The novel information-driven smoothing spline linearization method offers superior accuracy for high-precision displacement sensors.
  • The method effectively mitigates noise and reveals true sensor nonlinearity.
  • This approach provides a significant advancement in displacement sensing technology.