Design of a high precision temperature measurement system based on artificial neural network for different thermocouple types

Abstract Many types of sensors are nonlinear in nature but require an output that is linear. If linear approximation is accepted, for a given accuracy level, noise and measurement errors are always present. Therefore, curve-fitting techniques are frequently required to average these effects. The problem of estimating the sensor’s input–output characteristics is being increasingly tackled using software techniques. This paper describes an experimental method for the estimation of nonlinearity, testing and calibrating of the different thermocouple types using artificial neural network (ANN) based algorithms integrated in a virtual instrument (VI). An ANN and a data acquisition board with designed signal conditioning unit are used for data optimization and to collect experimental data, respectively. In both training and testing phases of the ANN, the Wavetek 9100 calibration unit is used to obtain experimental data. After the successful training completion of the ANN, it is then used as a neural linearizer to calculate the temperature from the thermocouple’s output voltage.

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[2]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[3]  Ivica Kostanic,et al.  Principles of Neurocomputing for Science and Engineering , 2000 .

[4]  L. Michalski,et al.  Temperature Measurement , 1985 .

[5]  Martin T. Hagan,et al.  Neural network design , 1995 .

[6]  Martin T. Hagan,et al.  Gauss-Newton approximation to Bayesian learning , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[7]  Magali R. G. Meireles,et al.  A comprehensive review for industrial applicability of artificial neural networks , 2003, IEEE Trans. Ind. Electron..

[8]  Alessandra Flammini,et al.  Transducer output signal processing using an optimal look-up table in microcontroller-based systems , 1997 .

[9]  Octavian Postolache,et al.  Minimizing temperature drift errors of conditioning circuits using artificial neural networks , 1998, IEEE Trans. Instrum. Meas..

[10]  L. R. Klopfenstein.  Software linearization techniques for thermocouples, thermistors, and RTDs , 1994 .

[11]  F. N. Trofimenkoff,et al.  Transducer output signal processing using an eight-bit microcomputer , 1986, IEEE Transactions on Instrumentation and Measurement.

[12]  Nicolás J. Medrano-Marqués,et al.  Sensor linearization with neural networks , 2001 .

[13]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[14]  D. Patranabis,et al.  Linearizing transducer characteristics , 1988 .

[15]  M. Attari,et al.  An artificial neural network to linearize a G (tungsten vs. tungsten 26% rhenium) thermocouple characteristic in the range of zero to 2000/spl deg/C , 1995, 1995 Proceedings of the IEEE International Symposium on Industrial Electronics.

[16]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[17]  Pasquale Daponte,et al.  Artificial neural networks in measurements , 1998 .

[18]  John G. Webster,et al.  Sensors and Signal Conditioning , 1993 .

[19]  Raúl Rojas,et al.  Neural Networks - A Systematic Introduction , 1996 .

[20]  John E. Dennis,et al.  Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.

[21]  B. S. Sonde,et al.  Transducer output signal processing using dual and triple microprocessor systems , 1989 .