Abstract
We present an artificial neural-network- (NN-) based smart interface framework for sensors operating in harsh environments. The NN-based sensor can automatically compensate for the nonlinear response characteristics and its nonlinear dependency on the environmental parameters, with high accuracy. To show the potential of the proposed NN-based framework, we provide results of a smart capacitive pressure sensor (CPS) operating in a wide temperature range of 0 to . Through simulated experiments, we have shown that the NN-based CPS model is capable of providing pressure readout with a maximum full-scale (FS) error of only over this temperature range. A novel scheme for estimating the ambient temperature from the sensor characteristics itself is proposed. For this purpose, a second NN is utilized to estimate the ambient temperature accurately from the knowledge of the offset capacitance of the CPS. A microcontroller-unit- (MCU-) based implementation scheme is also provided.
Highlights
In many practical application areas of avionics, automobiles, robotics, missile guidance, oil drilling, and industrial measurements, sensors operate in harsh environments such as extreme ambient temperature, pressure, humidity, and so forth
An important contribution of this paper is that we have proposed a novel scheme to estimate the ambient temperature from the sensor characteristics itself, using a second multilayer perceptron (MLP), eliminating the need of a separate temperature sensor
Smart sensors operating in harsh environments should be capable of providing accurate readout and autocompensation of the nonlinear influence of the environmental parameters on its response characteristics
Summary
In many practical application areas of avionics, automobiles, robotics, missile guidance, oil drilling, and industrial measurements, sensors operate in harsh environments such as extreme ambient temperature, pressure, humidity, and so forth In such situations, the response of the sensors depends on the measurand and on the environmental parameters in a nonlinear manner. In order to compensate for some of the nonidealities and to obtain accurate readout, several digital and analog interface circuits have been proposed in the past with some success [1, 2, 3, 4, 5, 6, 7] These techniques include both iterative and noniterative algorithms, and involve complex analog and/or digital signal processing to model the sensor characteristics. They provide a limited solution to the complex problem under the assumptions that the range of variation of environmental parameters is small and that the influence of the environmental parameters on the sensor characteristics is linear
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