Paper Details: Downloads: 415
Serial Number: P1110430003
Title: Neural Networks-based Fault Detection with Application in Ink Jet Printer
Authors: X. Z. Gao and S. J. Ovaska and X. Wang
Abstract: In this paper, we explore the feasibility of using both feedforward and Elman neural networks to detect assembly faults in ink jet printers. The method is an extension of the motor fault detection scheme recently proposed by the authors. Two types of cartridge faults are studied here: encoder belt misalignment and encoder strip error. These two faults are detected from the characteristics variants in the neural networks-based prediction of cartridge velocity signals. Simulation results demonstrate that neural networks can be trained to effectively detect the inherent encoder faults. Some discussions on the selection of appropriate fault detection criteria are also given.
Keywords: Fault detection, Elman neural network, feedforward neural network, prediction, ink jet printers.
Journal/Conference: International Journal of Automatic Control and System Engineering
Volume: 5
Issue: 1
Submission Date: 8/1/2004 12:00:00 AM
Review Date: 10/1/2004 12:00:00 AM
Publishing Date: 3/1/2005 12:00:00 AM
Article Downloads: 415
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