Dimension-Reduced Analog—Digital Mixed Measurement Method of Inductive Proximity Sensor

Abstract

Inductive proximity sensors (IPSs) present a unique no-contact advantage. They are widely preferred for displacement measurement in various industrial fields (e.g., aviation and aerospace), and they are improved continuously. When the inductance and resistance components of the IPS sensing core are separated, the influence of temperature drift on measurement can be eliminated. The complexity of online computation of component separation can be reduced using a two-dimensional look-up table method. However, this method exhibits disadvantages, such as large capacity of the look-up table, dependency on precision measurement of sensing core parameter, and nonlinear distribution of measurement resolution. This study aims to overcome these disadvantages by examining the nonlinear relationship between the response of the sensing core and the ambient temperature, and proposes a dimension-reduced measurement method. The proposed method extracts the characteristics of the response curves at two temperatures and calculates the characteristics of the response curves at any temperature using a linear approximation. The look-up table capacity is less than 0.37% of the two-dimensional look-up table capacity (condensed) under the same condition; dimension reduction enables the construction of a complete look-up table directly by calibration procedures and avoids precise measurement on sensing core parameters; the calibration procedures establish uniform mapping of the distribution of measurement resolution. The experiment shows that, when the measurement ranges are 0–6, 0–5, and 0–4 mm, the maximum measurement errors are 0.140, 0.065, and 0.040 mm, respectively, under temperature ranging from 20 ∘C to 110 ∘C. This study extends the measurement range from 0–5 mm to 0–7 mm and improves the measurement accuracy over 0.1 mm (50% at 5 mm) compared with the two-dimensional look-up table method. Therefore, the proposed method not only inherits the advantages of the original method but also achieves the above-mentioned expected capacity improvements effectively.

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

“…Based on the measured voltage U 1M ,Û 1ML andÛ 1MP denote the voltage computed by the LUT method and the proposed method, respectively. The difference between the measured U 1M and the computedÛ 1ML of the LUT method [28] is defined as ∆Û 1ML . The difference between the measured U 1M and the computedÛ 1MP of the proposed method is defined as ∆Û 1MP , shown as:…”

“…The difference between the target distance D x andD xL of the LUT method [28] is defined as ∆D xL . The difference between D x andD xP of the method proposed in this paper is defined as ∆D xP , which can be shown as:…”

“…Sampling U C in Equation (2) at time t time = t 2 and t time = t 1 [28], we can obtain U 2 , U 1 as follows:…”

“…To analyze the temperature effect and computational complexity, we use two methods: the theoretical method for the model in Figure 2 and the linear LUT based method in [28]. The comparison between the computed U 1 by the theoretical method and the estimatedÛ 1L by the LUT method in [28] when L x = 5 mH is shown in Figure 3. The average value of In the red area of Figure 3b, when −55 • C ≤ t ≤ 16 • C, ∆U 1L ≥ 0.5 LSB.…”

“…Whereas the influence of the temperature drift is strongly reduced, the operational temperature range is limited from 0 • C to 50 • C. Furthermore, it increases the complexity of the structure, which adds two different inductances connected in series to the quartz crystal [26]. The measurement methods of [27,28] were proposed for online calculation with low hardware resources. Both of these methods achieve high accuracy at room temperature, but show large deviation at a low temperature.…”