Neural network and trend prediction for technological processes monitoring

  • Authors:

  • Luis Paster Sanchez Fernandez;Oleksiy Pogrebnyak;Cornelio Yanez Marquez

  • Affiliations:

  • Center for Computing Research, National Polytechnic Institute, Mexico City, Mexico;Center for Computing Research, National Polytechnic Institute, Mexico City, Mexico;Center for Computing Research, National Polytechnic Institute, Mexico City, Mexico

  • Venue:
  • MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
  • Year:
  • 2005

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Abstract

The goal of this paper is to introduce an efficient predictive supervisory method for the trending of variables of technological processes and devices, with low run-time, for periodic analysis of high frequency, relatively (periods smaller than a second). This method allows to predict the time in which a process variable will arrive to an abnormal or important values. The data obtained in real time for each variable are used to estimate the parameters of a mathematical model. This model is continuous and of first-order or second-order (critically damped, overdamped or underdamped). An optimization algorithm is used for estimating the parameters. Before performing the estimation, the most appropriate model is determined by means of a feed-forward neural network.