Residual-based fault detection using soft computing techniques for condition monitoring at rolling mills

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• 作者：Francisco Serdio ; Edwin Lughofer ; Kurt Pichler ; Thomas Buchegger ; Hajrudin Efendic
• 关键词：Residual-based fault detection ; System identification ; Genetic Box&ndash ; Cox ; Fuzzy systems extraction ; On-line dynamic residual analysis
• 刊名：Information Sciences
• 出版年：20 February, 2014
• 年：2014
• 卷：259
• 期：Complete
• 页码：304-320
• 全文大小：3103 K

文摘

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. 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. 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.