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Study Findings from Johannes Kepler University Provide New Insights into Information Technology

February 11, 2014

By a News Reporter-Staff News Editor at Information Technology Newsweekly -- Investigators discuss new findings in Information Technology. According to news reporting originating in Linz, Austria, by VerticalNews journalists, research stated, "We propose a residual-based approach for fault detection at rolling mills based on data-driven soft computing techniques. It transforms the original measurement signals into a model space by identifying the multi-dimensional relationships contained in the system."

The news reporters obtained a quote from the research from Johannes Kepler University, "Residuals, calculated as deviations from the identified relations and normalized with the model uncertainties, are analyzed on-line with incremental/decremental statistical techniques. The identification of the models and the fault detection concept are conducted solely based on the on-line recorded data streams. Thus, neither annotated samples nor fault patterns/models, which are often very time-intensive and costly to obtain, need to be available a priori. As model architectures, we used pure linear models, a new genetic variant of Box-Cox models (termed as Genetic Box-Cox) reflecting weak non-linearities and Takagi-Sugeno fuzzy models being able to express more complex non-linearities, which are trained with sparse learning techniques. This choice gives us a clue about the degree of non-linearity contained in the system."

According to the news reporters, the research concluded: "Our approach is compared with several state-of-the-art approaches including a PCA-based approach, a univariate time-series analysis, a one-class SVM (fault-free) pattern recognizer in the signal space and a combined approach based on time-series model parameter changes."

For more information on this research see: Residual-based fault detection using soft computing techniques for condition monitoring at rolling mills. Information Sciences, 2014;259():304-320. Information Sciences can be contacted at: Elsevier Science Inc, 360 Park Ave South, New York, NY 10010-1710, USA. (Elsevier -; Information Sciences -

Our news correspondents report that additional information may be obtained by contacting F. Serdio, Johannes Kepler Univ Linz, Inst Design & Control Mechatron Syst, A-4040 Linz, Austria. Additional authors for this research include E. Lughofer, K. Pichler, T. Buchegger and H. Efendic.

Keywords for this news article include: Linz, Europe, Austria, Genetics, Information Technology

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Source: Information Technology Newsweekly

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