Development of an intelligent pressure measuring technique for bellows using radial basis function neural network

This paper presents the implementation of an intelligent pressure transmitter to measure pressure using a bellow sensor. In industrial applications, the deflection of the bellow due to applied pressure must be translated into an efficient electrical readout for monitoring, transmission and control. An inductive pick-up is used to convert the deflection of the bellow into the change in self-inductance of a coil. The signal conditioning circuit designed for the bellow sensor with the inductive coil is an inductance to voltage conversion circuit (ITVCC). The stray inductances, component tolerances and ambient factors introduce errors in the output of the ITVCC. The voltage-pressure relation exhibits a considerable nonlinearity and limits the measurement to local operations. In this aspect, we propose an artificial neural network (ANN) using a radial basis function to estimate and compensate the nonlinearity of the ITVCC. The intelligence of ANN modeling is incorporated into an embedded plug-in-module (EPIM). The output voltage of the EPIM is converted into a 4-20 mA current signal for further processing. The performance of the proposed technique is experimentally verified. The nonlinearity expressed as maximum deviation from the desired response is within ±0.6% of full scale reading. The design aspects, simulation analysis and experimental results of the technique are reported.