David Camarena-Martinez

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Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary(More)
Over the past few years, power quality (PQ) monitoring has become an important topic because of the negative impact of different machines to the electrical network and to the susceptibility of critical equipment. There are different disturbances that affect the PQ; therefore, in order to apply a proper solution, these have to be correctly detected and(More)
The development and application of techniques and methodologies for the analysis of power quality signals that offer a more efficient and reliable analysis in terms of processing and performance are still issues for industrial and academic fields, mainly considering the quick growing of the power quality (PQ) data in modern power systems. In this regard, an(More)
Induction motors, important elements into the industry, are susceptible to faults during its lifetime service; yet, they can keep working without affecting the process, but increasing the production costs as they consume more electrical current. Broken rotor bars (BRB) detection is an important topic due to the fact that this failure is silent and produces(More)
Detection of power quality disturbances (PQD) has become an important concern due to the increasing number of disturbing loads connected to the power line and to the susceptibility of some loads to the presence of these disturbances. Nowadays, this detection becomes more complicated since several disturbances can appear simultaneously. In this paper, a new(More)
Detection of failures in induction motors is one of the most important concerns in industry. An unexpected fault in the induction motors can cause a loss of financial resources and waste of time that most companies cannot afford. The contribution of this paper is a fusion of the Empirical Mode Decomposition (EMD) and Multiple Signal Classification (MUSIC)(More)
This paper presents a new EEMD-MUSIC- (ensemble empirical mode decomposition-multiple signal classification-) based methodology to identify modal frequencies in structures ranging from free and ambient vibration signals produced by artificial and natural excitations and also considering several factors as nonstationary effects, close modal frequencies, and(More)
Induction motors, vital elements into the industry, are more likely to be influenced by different faults during their lifetime service. Even when they can keep working without affecting the line processes, in most cases, an increase in the production costs usually occurs. Bearing fault detection is an important topic due to the fact that this failure yields(More)