Neural network based industrial processes monitoring

  • Authors:

  • Luis P. Sánchez-Fernández;Cornelio Yáñez-Márquez;Oleksiy Pogrebnyak

  • Affiliations:

  • Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City, Mexico;Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City, Mexico;Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City, Mexico

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2006

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Abstract

This industrial processes monitoring based on a neural network presents low run-time, and it useful for critical time tasks with periodic processing. This method allows the time prediction in which a variable will arrive to abnormal or important values. The data of each variable are used to estimate the parameters of a continuous mathematical model. At this moment, four models are used: first-order or second-order in three types (critically damped, overdamped or underdamped). An optimization algorithm is used for estimating the model parameters for a dynamic response to step input function, because this is the most frequent disturbance. Before performing the estimation, the most appropriate model is determined by means of a feed-forward neural network.