The article presents a new discretization method of a continuous-time linear model of sensor dynamics. It can be useful to reduce measuring errors related to the inertia of the sensor. For example it is important in the measurement of rapid processes as temperature changes in combustion chambers, or for shortening the time needed to establish the sensor readings in a transition state. There is assumed that sensor dynamics can be approximated by linear differential equation or transfer function. The searched coefficients of equivalent difference equation or discrete transfer function are obtained from Taylor expansion of a sensor output signal and then on the solution of the linear set of equations. The method does not require decomposition of sensor transfer function for zeros and poles and can be applied to the case of transfer function with zeros equal to zero. The method was used to compensate the dynamics of sensor measuring fast signals. The Bode characteristics of a compensator were compared with others derived using classical methods of discretization of linear models. Additionally, signals in time were presented to show the dynamic error before and after compensation.

The article presents the prototype of a measurement system with a hot probe, designed for testing thermal parameters of heat insulation materials. The idea is to determine parameters of thermal insulation materials using a hot probe with an auxiliary thermometer and a trained artificial neural network. The network is trained on data extracted from a nonstationary two-dimensional model of heat conduction inside a sample of material with the hot probe and the auxiliary thermometer. The significant heat capacity of the probe handle is taken into account in the model. The finite element method (FEM) is applied to solve the system of partial differential equations describing the model. An artificial neural network (ANN) is used to estimate coefficients of the inverse heat conduction problem for a solid. The network determines values of the effective thermal conductivity and effective thermal diffusivity on the basis of temperature responses of the hot probe and the auxiliary thermometer. All calculations, like FEM, training and testing processes, were conducted in the MATLAB environment. Experimental results are also presented. The proposed measurement system for parameter testing is suitable for temporary measurements in a building site or factory.