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In this paper an artificial intelligence based framework for fault detection and diagnosis to support supervision of the cardboard production is presented. Cutting accuracy significantly affects the quality of the product and because there are many different causes of errors, their identification requires a sound knowledge and experience of the service staff. The authors observed that the sources of errors can be characterized by a probability density function (pdf) of these errors. Therefore, they proposed a diagnostic method based on classification of sources of disturbances via the analysis of pdf calculated by a kernel density estimator. The multilayer perceptron is proposed as a classifier. Classification procedure is discussed with emphasis on generalization properties of the classifier. The application for data acquired from a real industrial process is presented.

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.