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Intelligent fault detection and diagnosis of a rotary cutoff in a corrugator

  • Jerzy Kasprzyk
  • , Stanislaw K. Musielak
  • BHS Corrugated Maschinen- und Anlagenbau GmbH

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper an artificial intelligence based framework for fault detection and diagnosis to support supervision of the cardboard production is presented. Cutting accuracy significantly affects the quality of the product and because there are many different causes of errors, their identification requires a sound knowledge and experience of the service staff. The authors observed that the sources of errors can be characterized by a probability density function (pdf) of these errors. Therefore, they proposed a diagnostic method based on classification of sources of disturbances via the analysis of pdf calculated by a kernel density estimator. The multilayer perceptron is proposed as a classifier. Classification procedure is discussed with emphasis on generalization properties of the classifier. The application for data acquired from a real industrial process is presented.

Original languageEnglish
Title of host publication2015 20th International Conference on Methods and Models in Automation and Robotics, MMAR 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1016-1021
Number of pages6
ISBN (Electronic)9781479987016
DOIs
Publication statusPublished - 29 Sept 2015
Event20th International Conference on Methods and Models in Automation and Robotics, MMAR 2015 - Miedzyzdroje, Poland
Duration: 24 Aug 201527 Aug 2015

Publication series

Name2015 20th International Conference on Methods and Models in Automation and Robotics, MMAR 2015

Conference

Conference20th International Conference on Methods and Models in Automation and Robotics, MMAR 2015
Country/TerritoryPoland
CityMiedzyzdroje
Period24/08/1527/08/15

Keywords

  • corrugated board machine
  • diagnostics
  • fault detection
  • neural network classifier
  • rotary cutoff
  • statistical kernel estimators

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering

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