An Intelligent Displacement Measurement Technique Using LVDT with an Optimized ANN

This paper aims at designing an intelligent displacement measurement technique by Linear Variable Differential Transformer (LVDT) using an optimized Artificial Neural Network (ANN). The objectives of the present work are to (i) extend the linearity range of measurement to 100% of input range, (ii) make the measurement technique adaptive of variation in (a) physical parameters of LVDT, (b) excitation frequency, and (iii) to achieve (i) and (ii) using an optimal neural network. An optimal ANN is considered by comparing five various schemes, algorithms, and number of hidden layers based on minimum mean square error (MSE) and Regression (R) close to 1. A suitable optimal ANN is added, replacing the conventional calibration circuit, in cascade to data conversion unit. The proposed technique provides linear relationship of the overall system over the full input range and makes it adaptive of variation in physical parameters of LVDT and excitation frequency. Since, the proposed intelligent displacement measurement technique produces output adaptive of variations in physical parameters of LVDT and excitation frequency, it avoids the requirement of repeated calibration every time the LVDT is replaced, and/or excitation frequency is changed. The optimized ANN is tested considering variations in physical parameters of LVDT and excitation frequency. These variations are considered within the specified ranges. Results show that the proposed technique has fulfilled the objectives.