20 August 1999 Real-time quality control of pipes using neural network prediction error signals for defect detection in time area
Author Affiliations +
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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alexander M. Akhmetshin, Alexander M. Akhmetshin, Andrey P. Gvozdak, Andrey P. Gvozdak, } "Real-time quality control of pipes using neural network prediction error signals for defect detection in time area", Proc. SPIE 3833, Intelligent Systems in Design and Manufacturing II, (20 August 1999); doi: 10.1117/12.359518; https://doi.org/10.1117/12.359518
PROCEEDINGS
7 PAGES


SHARE
Back to Top