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Background:  Displacement is often used as an indirect indicator for monitoring multiple parameters, i.e. force, velocity, acceleration, and weight, making it an important variable for the measurement and control of processes. Sensors such as Linear Variable Differential Transformers (LVDTs) play a primary role in the design of any displacement measuring instrument. Calibration of an instrument is carried out to produce accurate results from the measuring instrument.    Methods:  The objective of this study is to calibrate the output of LVDT by designing a signal conditioning circuit so as to extend the linearity range of the sensor to 100% of the full scale input range, and also allows the measurement technique to adapt to variations in the physical parameters of the LVDT, the supply frequency, and the temperature. An optimized neural network is trained to produce linear and adaptive output from the raw data obtained from LVDT. Optimization is achieved by choosing the best neural network algorithm, number of hidden layers and transfer function of neurons which produce the least mean square error. The optimized neural network algorithm is implemented on a Field Programmable Gate Array (FPGA) chip for testing and validation in real life.    Results:  Experimental results show that the proposed technique was able to extend the linearity of LVDT and make the output adaptive for variations in physical parameters of LVDT, supply frequency and temperature.    Conclusions:  Accurate measurement of displacement is essential in many process applications, and a good calibration technique is required to produce accurate measurement. The presented calibration technique using optimized neural network algorithms has produced reliable measurements as desired.

Field programmable gate array implementation of an intelligent soft calibration technique for linear variable differential transformers