Dynamic neural network calibration of quartz transducers

The performance of quartz pressure transducers is often compromised when subjected to thermal transients. In this paper, a new method of calibrating quartz transducers in which a neural network is used to accomplish a dynamic calibration of a transducer pair for a range of temperatures and pressures will be described. When a quartz pressure transducer is exposed to an environmental temperature change, thermal stress is induced in the sensing element. This thermal stress causes erroneous pressure readings until thermal equilibrium is achieved within the transducer. In order to compensate the undesirable transient thermal effects on the pressure transducer performance, multiple past and future temperature-and-pressure sensor outputs are used as inputs to a calibrating neural network. This new method allows the time-dependent qualities of the quartz-oscillator transducer to be included in the calibration scheme. This method can be applied generally to other types of transducers that are subjected to transient environmental effects and is not limited to quartz-oscillator transducers. A theoretical discussion of this technology is presented, followed by experimental results obtained by applying the method to quartz pressure transducer calibration. Copyright © 2002 John Wiley & Sons, Ltd.