DEVELOPMENT OF AN ARTIFICIAL NEURAL NETWORK FOR INFORMATION AND MEASUREMENT SYSTEM FOR CONTROLLING THE GEOMETRIC DIMENSIONS OF POWER EQUIPMENT

The paper deals with the development of an artificial neural network for compensating for nonkinematic errors of an information and measurement system (IMS) based on a coordinate measuring arm (CMA). After compensating for kinematic errors using a mathematical model, the proposed back-propagation neural network corrects non-kinematic errors arising from thermal deformations, noise, and element deformation inaccuracies. Experimental studies conducted on synthetic data demonstrated a significant reduction in the mean square error (MSE) of the coordinates of the measured points and a decrease in measurement uncertainty. The model exhibited high accuracy and stability, which confirms its effectiveness for controlling the geometric parameters of energy equipment. Keywords: artificial neural network, error back propagation, error compensation, information and measurement system, coordinate measuring arm, geometric parameters, modeling.