Condition Monitoring at Rolling Mills with Data-Driven Residual-Based Fault Detection
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
IFAC Conference on Manufacturing Modeling, Management and Control (MIM) 2013
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
In this paper, we propose a residual-based approach for fault detection at rolling
mills, which is based on data-driven soft computing techniques. The basic idea is to transform the original measurement signals into a feature space by identifying multi-dimensional relationships
contained in the system, representing the nominal fault-free case and analyzing residuals with incremental/decremental statistical techniques. The identification of the models and the fault
detection are conducted in completely unsupervised manner, 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)
reecting weak non-linearities and Takagi-Sugeno fuzzy models being able to express more complex non-linearities, which are trained with an extended version of SparseFIS. Our approach
will be compared with a renowned state-of-the-art approach using PCA components directions based on three different typical scenarios on rolling mill production.