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

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.