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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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 now 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.
Publisher
Korean Society of Mechanical Engineers
Issue Date
2002-12
Language
English
Article Type
Article
Citation

KSME INTERNATIONAL JOURNAL, v.16, no.12, pp.1604 - 1612

ISSN
1226-4865
URI
http://hdl.handle.net/10203/190089
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