Mario Eltabach

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Like all mechanical devices, motors are subject to failures, which can sometimes lead to the shutting down of an entire industrial process. This paper looks at failure predictions in threephase line-operated induction machines through spectral analysis of electric and electromagnetic signals. Fault characteristics frequencies generated in the estimated and(More)
Rotor asymmetries in induction machines perturb many components such as, flux patterns and electromagnetic torque. The supervision of these signals enables early detection of such faults and help to machine diagnostic. This paper studies the detection of rotor imperfection by spectral analysis of the electromagnetic torque computed by two rotor flux(More)
This paper reports a comparative investigation of different diagnostic procedures that use input electric signals to detect different types of abnormalities in rotating machinery. Besides the traditional one phase current spectrum analysis, the diagnostic procedures based on spectrum analysis of the instantaneous partial powers, total power, and finally the(More)
Noise radiated by rotating and reciprocating machines is often a mixture of multiple complex sources, the successful reduction of which is a field of intensive research. In this paper an advanced source separation approach is presented, based on cyclic Wiener filtering, which takes into account the cyclostationarity property of the signals. The aim of the(More)
Robotic cooperative tasks impose, in many cases, a grasping action. Grasping by coiling it is one of the most versatile action. The present article propose a frequency stability criterion based on the Kahman – Yakubovich – Popov Lemma for the hyper-redundant arms with continuum element that performs the grasping function by coiling. Dynamics of the(More)
In this paper, we present a content-based indexing and retrieval (CBIR) system for osteo-articular MRI application, devoted to student formation and diagnosis help. A novel approach for image retrieval, called EMiner is introduced. It is based on a semi-supervised learning method using mixture models. We have also introduced a variable selection mechanism(More)
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