A Non-Linear Temperature Compensation Model for Improving the Measurement Accuracy of an Inductive Proximity Sensor...

Area of Science:

• Electrical Engineering
• Sensor Technology
• Measurement Systems

Background:

• Non-contacting Inductive Proximity Sensors (IPS) exhibit non-linear behavior with temperature, impacting real-time distance measurement accuracy.
• Existing linear models for IPS distance estimation show significant deviations, particularly at low temperatures.

Purpose of the Study:

• To develop and validate a non-linear measurement model for accurate real-time distance computation using IPS sensors.
• To address the temperature-dependent non-linear characteristics of IPS systems within a broad operational range.

Main Methods:

• A non-linear polynomial algorithm was developed to model the temperature effects on IPS measurements, replacing traditional linear Look-Up Table (LUT) methods.
• The proposed model was implemented in a 0.18 μm CMOS process and packaged as an Application-Specific Integrated Circuit (ASIC).

Main Results:

• The non-linear model achieved accurate real-time distance computation for IPS sensors across a wide temperature range (-55 °C to 125 °C).
• For a common sensing distance of 4 mm, the ASIC chips demonstrated a computed distance deviation within ±0.2 mm.
• The model successfully compensated for temperature-induced non-linearities, outperforming previous linear methods.

Conclusions:

• The proposed non-linear temperature compensation model enables high-accuracy, real-time distance measurement with Inductive Proximity Sensors.
• The developed ASIC solution offers low hardware resource consumption and reliable performance in extreme temperature conditions.
• This advancement is crucial for applications requiring precise distance sensing in variable thermal environments.