» Articles » PMID: 32899388

A Non-Linear Temperature Compensation Model for Improving the Measurement Accuracy of an Inductive Proximity Sensor and Its Application-Specific Integrated Circuit Implementation

Overview
Journal Sensors (Basel)
Publisher MDPI
Specialty Biotechnology
Date 2020 Sep 9
PMID 32899388
Citations 1
Authors
Affiliations
Soon will be listed here.
Abstract

The non-linear characteristic of a non-contacting Inductive Proximity Sensor (IPS) with the temperature affects the computation accuracy when measuring the target distance in real time. The linear model based method for distance estimation shows a large deviation at a low temperature. Accordingly, this paper presents a non-linear measurement model, which computes the target distance accurately in real time within a wide temperature range from -55 °C to 125 °C. By revisiting the temperature effect on the IPS system, this paper considers the non-linear characteristic of the IPS measurement system due to the change of temperature. The proposed model adopts a non-linear polynomial algorithm rather than the simple linear Look-Up Table (LUT) method, which provides more accurate distance estimation compared to the previous work. The introduced model is fabricated in a 0.18 μm Complementary Metal Oxide Semiconductor (CMOS) process and packaged in a CQFN40. For the most commonly used sensing distance of 4 mm, the computed distance deviation of the Application-Specific Integrated Circuit (ASIC) chips falls within the range of [-0.2,0.2] mm. According to the test results of the ASIC chips, this non-linear temperature compensation model successfully achieves real-time and high-accuracy computation within a wide temperature range with low hardware resource consumption.

Citing Articles

Effect of Excitation Signal on Double-Coil Inductive Displacement Transducer.

Li Y, Li R, Yang J, Xu J, Yu X Sensors (Basel). 2023; 23(7).

PMID: 37050839 PMC: 10098683. DOI: 10.3390/s23073780.

References
1.
Guo Y, Lai C, Shao Z, Xu K, Li T . Differential Structure of Inductive Proximity Sensor. Sensors (Basel). 2019; 19(9). PMC: 6539788. DOI: 10.3390/s19092210. View

2.
Podhraski M, Trontelj J . A Differential Monolithically Integrated Inductive Linear Displacement Measurement Microsystem. Sensors (Basel). 2016; 16(3). PMC: 4813959. DOI: 10.3390/s16030384. View

3.
Guo Y, Shao Z, Tao H, Xu K, Li T . Dimension-Reduced Analog-Digital Mixed Measurement Method of Inductive Proximity Sensor. Sensors (Basel). 2017; 17(7). PMC: 5539794. DOI: 10.3390/s17071533. View

4.
Guo Y, Shao Z, Li T . An Analog-Digital Mixed Measurement Method of Inductive Proximity Sensor. Sensors (Basel). 2016; 16(1). PMC: 4732063. DOI: 10.3390/s16010030. View

5.
Chlenova A, Moiseev A, Derevyanko M, Semirov A, Lepalovsky V, Kurlyandskaya G . Permalloy-Based Thin Film Structures: Magnetic Properties and the Giant Magnetoimpedance Effect in the Temperature Range Important for Biomedical Applications. Sensors (Basel). 2017; 17(8). PMC: 5579517. DOI: 10.3390/s17081900. View