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Application of logistic regression for fault analysis in an industrial printing process

Sutanto, E., Warwick, K. and Griffin, M. (1992) Application of logistic regression for fault analysis in an industrial printing process. In: 9th IEEE Instrumentation and Measurement Technology Conference, 1992. IEEE, pp. 675-680. ISBN 0780306406

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To link to this item DOI: 10.1109/IMTC.1992.245054


A statistical technique for fault analysis in industrial printing is reported. The method specifically deals with binary data, for which the results of the production process fall into two categories, rejected or accepted. The method is referred to as logistic regression, and is capable of predicting future fault occurrences by the analysis of current measurements from machine parts sensors. Individual analysis of each type of fault can determine which parts of the plant have a significant influence on the occurrence of such faults; it is also possible to infer which measurable process parameters have no significant influence on the generation of these faults. Information derived from the analysis can be helpful in the operator's interpretation of the current state of the plant. Appropriate actions may then be taken to prevent potential faults from occurring. The algorithm is being implemented as part of an applied self-learning expert system.

Item Type:Book or Report Section
ID Code:21731
Uncontrolled Keywords:binary data, fault analysis, fault occurrences, industrial printing, logistic regression, machine parts sensors, prediction, production process, self-learning expert system, statistical technique

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