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Proceedings Paper

Real-time quality control of pipes using neural network prediction error signals for defect detection in time area
Author(s): Alexander M. Akhmetshin; Andrey P. Gvozdak
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Paper Abstract

The magnetic-induction method of quality control of seamless pipes in real-time characterized by a high level of structural noises having the composite law of an elementary probability law varying from batch to a batch, of a varying form. The traditional method of a detection of defects of pipes is depend to usage of ethanol defects. However shape of actual defects is casual, that does not allow to use methods of an optimum filtration for their detection. Usage of adaptive variants of a Kalman filter not ensures the solutions of a problem of a detection because of poor velocity of adaptation and small relation a signal/the correlated noise. For the solution of a problem was used structural Adaptive Neuro-Fuzzy Inference System (ANFIS) which was trained by delivery of every possible variants of signals without defects of sites of pipes filed by transducer system. As an analyzable signal the error signal of the prognosis ANFIS was considered. The carried out experiments have shown, that the method allows to ooze a signal of casual extended defects even in situations when a signal-noise ratio was less unity and the traditional amplitudes methods of selection of signals of defects did not determine.

Paper Details

Date Published: 20 August 1999
PDF: 7 pages
Proc. SPIE 3833, Intelligent Systems in Design and Manufacturing II, (20 August 1999); doi: 10.1117/12.359518
Show Author Affiliations
Alexander M. Akhmetshin, Dnipropetrovsk State Univ. (Ukraine)
Andrey P. Gvozdak, Dnipropetrovsk State Univ. (Ukraine)


Published in SPIE Proceedings Vol. 3833:
Intelligent Systems in Design and Manufacturing II
Bhaskaran Gopalakrishnan; San Murugesan, Editor(s)

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