Artificial Neural Networks (ANN) In data pattern recognition for monitoring purpose

Abstract: This thesis is meant to be a simple introduction to the field of the Artificial Neural Networks and a survey of the possibility of using neural networks in fault diagnosis of sand moulding machines manufactured by DISA. The purpose of the survey is to give an indication of the direction the future work should take in order to obtain a sustainable solution for the use of artificial neural networks for monitoring molding machines in industrial plants. A description of the sand moulding process and DISA’s current fault detection and diagnosis procedure is given together with testing the potentials of feed-forward neural networks for recognizing patterns represented in control charts based on data of 16 sampled channels on the DISAMATIC moulding machine. The testing in Matlab and Encog environment proved that neural networks can learn to recognize periodic patterns in presented data but accepts too large deviations in patterns. The concluding part in this work reveals that an application based solely on neural networks, is not the sustainable solution and some prior signal processing of the sampled input is necessary.