Abstract
In this paper a proposal for solving the problem of diagnostics of cutting errors in a rotary cutoff in a corrugated board machine processing line is presented. There are many different reasons for errors, and their identification requires a sound knowledge and experience of the service staff. The authors, using their many years’ experience and a huge database, have found that many sources of errors can be characterized using a probability density function (pdf). They proposed a diagnostics method based on classification of sources of disturbances using the analysis of pdf determined with a kernel density estimator. Multilayer feedforward neural network is proposed as a classifier. Classification procedure is discussed, together with research results based on data from real industrial processes.
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Kasprzyk, J., Musielak, S.K. (2014). Fault Diagnosis of a Corrugator Cut-off Using Neural Network Classifier. In: Swiątek, J., Grzech, A., Swiątek, P., Tomczak, J. (eds) Advances in Systems Science. Advances in Intelligent Systems and Computing, vol 240. Springer, Cham. https://doi.org/10.1007/978-3-319-01857-7_35
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DOI: https://doi.org/10.1007/978-3-319-01857-7_35
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01856-0
Online ISBN: 978-3-319-01857-7
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