A study on the fault diagnosis of roller-shape using frequency analysis of tension signals and artificial neural networks based approach in a web transport system |
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Authors: | Email author" target="_blank">Kyung-Mo?TahkEmail author Kee-Hyun?Shin |
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Affiliation: | (1) Satellite Technology Research Center, KAIST, 305-701 Daejeon, Korea;(2) School of Mechanical and Aerospace Engineering, Konkuk University, 143-701 Seoul, Korea |
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Abstract: | Rollers in the continuous process systems are ones of key components that determine the quality of web products. The condition
of rollers (e.g. eccentricity, runout) should be consistently monitored in order to maintain the process conditions (e.g.
tension, edge position) within a required specification. In this paper, a new diagnosis algorithm is suggested to detect the
defective rollers based on the frequency analysis of web tension signals. The kernel of this technique is to use the characteristic
features (RMS, Peak value, Power spectral density) of tension signals which allow the identification of the faulty rollers
and the diagnosis of the degree of fault in the rollers. The characteristic features could be used to train an artificial
neural network which could classify roller conditions into three groups (normal, warning, and faulty conditions). The simulation
and experimental results showed that the suggested diagnosis algorithm can be successfully used to identify the defective
rollers as well as to diagnose the degree of the defect of those rollers. |
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Keywords: | Web Web Transport System Web Tension Roller/Roll Fault Diagnosis Artificial Neural Network |
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