Empirical Mode Decomposition Analysis for Broken-Bar Detection on Squirrel Cage Induction Motors

2015 ◽  
Vol 64 (5) ◽  
pp. 1118-1128 ◽  
Author(s):  
Ricardo Valles-Novo ◽  
Jose de Jesus Rangel-Magdaleno ◽  
Juan Manuel Ramirez-Cortes ◽  
Hayde Peregrina-Barreto ◽  
Roberto Morales-Caporal
Keyword(s):  
Empirical Mode Decomposition ◽  
Induction Motors ◽  
Decomposition Analysis ◽  
Squirrel Cage ◽  
Mode Decomposition ◽  
Broken Bar Detection

Empirical Mode Decomposition Analysis for Visual Stylometry

2012 ◽  
Vol 34 (11) ◽  
pp. 2147-2157 ◽  
Author(s):  
J. M. Hughes ◽  
Dong Mao ◽  
D. N. Rockmore ◽  
Yang Wang ◽  
Qiang Wu
Keyword(s):  
Empirical Mode Decomposition ◽  
Decomposition Analysis ◽  
Mode Decomposition

scholarly journals Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
David Camarena-Martinez ◽  
Martin Valtierra-Rodriguez ◽  
Arturo Garcia-Perez ◽  
Roque Alfredo Osornio-Rios ◽  
Rene de Jesus Romero-Troncoso
Keyword(s):  
Empirical Mode Decomposition ◽  
High Performance ◽  
Induction Motors ◽  
Industrial Applications ◽  
Feed Forward Neural Network ◽  
Linear Network ◽  
Multiple Faults ◽  
Online Systems ◽  
Time Operation ◽  
Mode Decomposition

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 and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications.

scholarly journals Ensemble Empirical Mode Decomposition Analysis of EEG Data Collected during a Contour Integration Task

PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0119489 ◽  
Author(s):  
Karema Al-Subari ◽  
Saad Al-Baddai ◽  
Ana Maria Tomé ◽  
Gregor Volberg ◽  
Rainer Hammwöhner ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Decomposition Analysis ◽  
Ensemble Empirical Mode Decomposition ◽  
Contour Integration ◽  
Mode Decomposition ◽  
Eeg Data

scholarly journals Fused Empirical Mode Decomposition and MUSIC Algorithms for Detecting Multiple Combined Faults in Induction Motors

2015 ◽  
Vol 13 (1) ◽  
pp. 160-167 ◽  
Author(s):  
D. Camarena-Martinez ◽  
R. Osornio-Rios ◽  
R.J. Romero-Troncoso ◽  
A. Garcia-Perez
Keyword(s):  
Empirical Mode Decomposition ◽  
Induction Motors ◽  
Mode Decomposition

scholarly journals On the multi-scale nature of large geomagnetic storms: an empirical mode decomposition analysis

2012 ◽  
Vol 19 (6) ◽  
pp. 667-673 ◽  
Author(s):  
P. De Michelis ◽  
G. Consolini ◽  
R. Tozzi
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Geomagnetic Activity ◽  
Geomagnetic Storms ◽  
Decomposition Analysis ◽  
Activity Level ◽  
Activity Levels ◽  
Geomagnetic Disturbances ◽  
Multi Scale ◽  
Mode Decomposition

Abstract. Complexity and multi-scale are very common properties of several geomagnetic time series. On the other hand, it is amply demonstrated that scaling properties of geomagnetic time series show significant changes depending on the geomagnetic activity level. Here, we study the multi-scale features of some large geomagnetic storms by applying the empirical mode decomposition technique. This method, which is alternative to traditional data analysis and is designed specifically for analyzing nonlinear and nonstationary data, is applied to long time series of Sym-H index relative to periods including large geomagnetic disturbances. The spectral and scaling features of the intrinsic mode functions (IMFs) into which Sym-H time series can be decomposed, as well as those of the Sym-H time series itself, are studied considering different geomagnetic activity levels. The results suggest an increase of dynamical complexity and multi-scale properties for intermediate geomagnetic activity levels.

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