Fundamental frequency suppression for the detection of broken bar in induction motors at low slip and frequency

Producción CientíficaBroken rotor bar (BRB) is one of the most common failures in induction motors (IMs) these days; however, its identification is complicated since the frequencies associated with the fault condition appear near the fundamental frequency component (FFC). This situation gets worse when the IM slip or the operation frequency is low. In these circumstances, the common techniques for condition monitoring may experience troubles in the identification of a faulty condition. By suppressing the FFC, the fault detection is enhanced, allowing the identification of BRB even at low slip conditions. The main contribution of this work consists of the development of a preprocessing technique that estimates the FFC from an optimization point of view. This way, it is possible to remove a single frequency component instead of removing a complete frequency band from the current signals of an IM. Experimentation is performed on an IM operating at two different frequencies and at three different load levels. The proposed methodology is compared with two different approaches and the results show that the use of the proposed methodology allows to enhance the performance delivered by the common methodologies for the detection of BRB in steady state.CONACyT scholarship (415315)Project FOFI-UAQ 2018 FIN201812PRODEP UAQ-PTC-385 gran

Removing micromelody from fundamental frequency contours

In this paper we describe a new method to diminish microprosodic components of fundamental frequency contours by applying weight functions linked to microprosodically classified phone combinations. For vowel segments in obstruent environments our algorithm outperforms standard smoothing algorithms like Moving-Average filtering, Savitzky-Golay filtering or MOMEL in diminishing F0 variations related to microprosodic factors while retaining significant differences related to macroprosody

PYIN: A FUNDAMENTAL FREQUENCY ESTIMATOR USING PROBABILISTIC THRESHOLD DISTRIBUTIONS

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Can one see the fundamental frequency of a drum?

We establish two-sided estimates for the fundamental frequency (the lowest eigenvalue) of the Laplacian in an open subset G of R^n with the Dirichlet boundary condition. This is done in terms of the interior capacitary radius of G which is defined as the maximal possible radius of a ball B which has a negligible intersection with the complement of G. Here negligibility of a subset F in B means that the Wiener capacity of F does not exceed gamma times the capacity of B, where gamma is an arbitrarily fixed constant between 0 and 1. We provide explicit values of constants in the two-sided estimates.Comment: 18 pages, some misprints correcte

Joint singing voice separation and F0 estimation with deep U-net architectures

Vocal source separation and fundamental frequency estimation in music are tightly related tasks. The outputs of vocal source separation systems have previously been used as inputs to vocal fundamental frequency estimation systems; conversely, vocal fundamental frequency has been used as side information to improve vocal source separation. In this paper, we propose several different approaches for jointly separating vocals and estimating fundamental frequency. We show that joint learning is advantageous for these tasks, and that a stacked architecture which first performs vocal separation outperforms the other configurations considered. Furthermore, the best joint model achieves state-of-the-art results for vocal-f0 estimation on the iKala dataset. Finally, we highlight the importance of performing polyphonic, rather than monophonic vocal-f0 estimation for many real-world cases