An Efficient, Memory-Saving Approach for the Loewner Framework.

The Loewner framework is one of the most successful data-driven model order reduction techniques. If is the cardinality of a given data set, the so-called Loewner and shifted Loewner matrices and can be defined by solely relying on information encoded in the considered data set and they play a crucial role in the computation of the sought rational model approximation.In particular, the singular value decomposition of a linear combination of and provides the tools needed to construct accurate models which fulfill important approximation properties with respect to the original data set.

Data-driven fault detection of open circuits in multi-phase inverters based on current polarity using Auto-adaptive and Dynamical Clustering.

This paper proposes a data-driven method for the detection and isolation of open-circuit faults in multi-phase inverters using measurements of the motor currents. First, feature variables are formulated in terms of the averages of the phase currents and their absolute values. Next, by using an AUto-adaptive and Dynamical Clustering (AUDyC) based on Gaussian Mixture Models, feature data is clustered into different classes characterizing normal and faulty operation modes.