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 Multiple Fault Detection of Rolling Bearing through Ensemble Empirical Mode Decomposition of Vibration Signal

2019 ◽  
Vol 9 (2) ◽  
pp. 2724-2726
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Driven ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Multiple Faults ◽  
Mode Decomposition

Generally, two or more faults occur simultaneously in the bearings. These Compound Faults (CF) in bearing, are most difficult type of faults to detect, by any data-driven method including machine learning. Hence, it is a primary requirement to decompose the fault vibration signals logically, so that frequencies can be grouped in parts. Empirical Mode Decomposition (EMD) is one of the simplest techniques of decomposition of signals. In this paper we have used Ensemble Empirical Mode Decomposition (EEMD) technique for compound fault detection/identification. Ensembled Empirical Mode Decomposition is found useful, where a white noise helps to detect the bearing frequencies. The graphs show clearly the capability of EEMD to detect the multiple faults in rolling bearings.

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

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

Design of high performance copyright protection watermarking based on lifting wavelet transform and bi empirical mode decomposition

2018 ◽  
Vol 77 (19) ◽  
pp. 24593-24614 ◽  
Author(s):  
Nidaa Hasan Abbas ◽  
Sharifah Mumtazah Syed Ahmad ◽  
Sajida Parveen ◽  
Wan Azizun Wan ◽  
Abd. Rahman Bin Ramli
Keyword(s):  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Copyright Protection ◽  
High Performance ◽  
Lifting Wavelet Transform ◽  
Mode Decomposition ◽  
Lifting Wavelet

EMPIRICAL MODE DECOMPOSITION METHOD FOR MEG PHANTOM DATA ANALYSIS

2009 ◽  
Vol 18 (08) ◽  
pp. 1467-1480
Author(s):  
JUHONG YANG ◽  
YUKI SAITO ◽  
QIWEI SHI ◽  
JIANTING CAO ◽  
TOSHIHISA TANAKA ◽  
...  
Keyword(s):  
Data Analysis ◽  
Human Brain ◽  
Empirical Mode Decomposition ◽  
High Performance ◽  
Invasive Technique ◽  
High Temporal Resolution ◽  
Current Dipole ◽  
Brain Functions ◽  
Mode Decomposition ◽  
High Level

Magnetoencephalography (MEG) is a powerful and non-invasive technique for measuring human brain activity with a high temporal resolution. The motivation for studying MEG data analysis is to extract the essential features from real-world measured data and represent them corresponding to the human brain functions. This usually depends on how to reduce a high level noise from the measurement. In this paper, a novel multistage MEG data analysis method based on the empirical mode decomposition (EMD) and independent component analysis (ICA) approaches is proposed for the feature extraction. Moreover, EMD and ICA algorithms are investigated for analyzing the MEG single-trial data which is recorded from the experiment of phantom. The analyzed results are presented to illustrate the effectiveness and high performance both in high level noise reduction by EMD associated with ICA approach and source localization by equivalent current dipole fitting method.

scholarly journals A Novel Method Based on Combination of Independent Component Analysis and Ensemble Empirical Mode Decomposition for Removing Electrooculogram Artifacts From Multichannel Electroencephalogram Signals

2021 ◽  
Vol 15 ◽  
Author(s):  
Chao-Lin Teng ◽  
Yi-Yang Zhang ◽  
Wei Wang ◽  
Yuan-Yuan Luo ◽  
Gang Wang ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Empirical Mode Decomposition ◽  
High Performance ◽  
Component Analysis ◽  
Ensemble Empirical Mode Decomposition ◽  
Independent Component ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Mode Decomposition ◽  
Multichannel Eeg

Electrooculogram (EOG) is one of common artifacts in recorded electroencephalogram (EEG) signals. Many existing methods including independent component analysis (ICA) and wavelet transform were applied to eliminate EOG artifacts but ignored the possible impact of the nature of EEG signal. Therefore, the removal of EOG artifacts still faces a major challenge in EEG research. In this paper, the ensemble empirical mode decomposition (EEMD) and ICA algorithms were combined to propose a novel EEMD-based ICA method (EICA) for removing EOG artifacts from multichannel EEG signals. First, the ICA method was used to decompose original EEG signals into multiple independent components (ICs), and the EOG-related ICs were automatically identified through the kurtosis method. Then, by performing the EEMD algorithm on EOG-related ICs, the intrinsic mode functions (IMFs) linked to EOG were discriminated and eliminated. Finally, artifact-free IMFs were projected to obtain the ICs without EOG artifacts, and the clean EEG signals were ultimately reconstructed by the inversion of ICA. Both EOGs correction from simulated EEG signals and real EEG data were studied, which verified that the proposed method could achieve an improved performance in EOG artifacts rejection. By comparing with other existing approaches, the EICA obtained the optimal performance with the highest increase in signal-to-noise ratio and decrease in root mean square error and correlation coefficient after EOG artifacts removal, which demonstrated that the proposed method could more effectively eliminate blink artifacts from multichannel EEG signals with less error influence. This study provided a novel promising method to eliminate EOG artifacts with high performance, which is of great importance for EEG signals processing and analysis.

scholarly journals Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 783 ◽  
Author(s):  
Martin Valtierra-Rodriguez ◽  
Juan Amezquita-Sanchez ◽  
Arturo Garcia-Perez ◽  
David Camarena-Martinez
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Condition Monitoring ◽  
Induction Motors ◽  
A Priori ◽  
Digital Signal ◽  
Decomposition Methods ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Mode Decomposition

Empirical mode decomposition (EMD)-based methods are powerful digital signal processing techniques because they do not need a priori information of the target signal due to their intrinsic adaptive behavior. Moreover, they can deal with non-linear and non-stationary signals. This paper presents the field programmable gate array (FPGA) implementation for the complete ensemble empirical mode decomposition (CEEMD) method, which is applied to the condition monitoring of an induction motor. The CEEMD method is chosen since it overcomes the performance of EMD and EEMD (ensemble empirical mode decomposition) methods. As a first application of the proposed FPGA-based system, the proposal is used as a processing technique for feature extraction in order to detect and classify broken rotor bar faults in induction motors. In order to obtain a complete online monitoring system, the feature extraction and classification modules are also implemented on the FPGA. Results show that an average effectiveness of 96% is obtained during the fault detection.

scholarly journals Multiple-Fault Detection Methodology Based on Vibration and Current Analysis Applied to Bearings in Induction Motors and Gearboxes on the Kinematic Chain

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Juan Jose Saucedo-Dorantes ◽  
Miguel Delgado-Prieto ◽  
Juan Antonio Ortega-Redondo ◽  
Roque Alfredo Osornio-Rios ◽  
Rene de Jesus Romero-Troncoso
Keyword(s):  
Induction Motors ◽  
Spectral Component ◽  
Kinematic Chain ◽  
Theoretical Models ◽  
Industrial Sector ◽  
Industrial Applications ◽  
Current Analysis ◽  
Multiple Faults ◽  
Bearing Defect ◽  
Component Identification

Gearboxes and induction motors are important components in industrial applications and their monitoring condition is critical in the industrial sector so as to reduce costs and maintenance downtimes. There are several techniques associated with the fault diagnosis in rotating machinery; however, vibration and stator currents analysis are commonly used due to their proven reliability. Indeed, vibration and current analysis provide fault condition information by means of the fault-related spectral component identification. This work presents a methodology based on vibration and current analysis for the diagnosis of wear in a gearbox and the detection of bearing defect in an induction motor both linked to the same kinematic chain; besides, the location of the fault-related components for analysis is supported by the corresponding theoretical models. The theoretical models are based on calculation of characteristic gearbox and bearings fault frequencies, in order to locate the spectral components of the faults. In this work, the influence of vibrations over the system is observed by performing motor current signal analysis to detect the presence of faults. The obtained results show the feasibility of detecting multiple faults in a kinematic chain, making the proposed methodology suitable to be used in the application of industrial machinery diagnosis.

scholarly journals Utilisation of Ensemble Empirical Mode Decomposition in Conjunction with Cyclostationary Technique for Wind Turbine Gearbox Fault Detection

2020 ◽  
Vol 10 (9) ◽  
pp. 3334 ◽  
Author(s):  
Sanaz Roshanmanesh ◽  
Farzad Hayati ◽  
Mayorkinos Papaelias
Keyword(s):  
Signal Processing ◽  
Wind Turbine ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Noisy Environment ◽  
Wind Turbine Gearbox ◽  
Multiple Faults ◽  
Mode Decomposition ◽  
Processing Techniques ◽  
Signal Processing Techniques

In this paper the application of cyclostationary signal processing in conjunction with Ensemble Empirical Mode Decomposition (EEMD) technique, on the fault diagnostics of wind turbine gearboxes is investigated and has been highlighted. It is shown that the EEMD technique together with cyclostationary analysis can be used to detect the damage in complex and non-linear systems such as wind turbine gearbox, where the vibration signals are modulated with carrier frequencies and are superimposed. In these situations when multiple faults alongside noisy environment are present together, the faults are not easily detectable by conventional signal processing techniques such as FFT and RMS.

scholarly journals Fast mode decomposition in few-mode fibers

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Egor S. Manuylovich ◽  
Vladislav V. Dvoyrin ◽  
Sergei K. Turitsyn
Keyword(s):  
Machine Learning ◽  
Intensity Distribution ◽  
High Performance ◽  
Phase Retrieval ◽  
Low Cost ◽  
Industrial Applications ◽  
Machine Learning Techniques ◽  
Decomposition Algorithms ◽  
Measured Intensity ◽  
Mode Decomposition

Abstract Retrieval of the optical phase information from measurement of intensity is of a high interest because this would facilitate simple and cost-efficient techniques and devices. In scientific and industrial applications that exploit multi-mode fibers, a prior knowledge of spatial mode structure of the fiber, in principle, makes it possible to recover phases using measured intensity distribution. However, current mode decomposition algorithms based on the analysis of the intensity distribution at the output of a few-mode fiber, such as optimization methods or neural networks, still have high computational costs and high latency that is a serious impediment for applications, such as telecommunications. Speed of signal processing is one of the key challenges in this approach. We present a high-performance mode decomposition algorithm with a processing time of tens of microseconds. The proposed mathematical algorithm that does not use any machine learning techniques, is several orders of magnitude faster than the state-of-the-art deep-learning-based methods. We anticipate that our results can stimulate further research on algorithms beyond popular machine learning methods and they can lead to the development of low-cost phase retrieval receivers for various applications of few-mode fibers ranging from imaging to telecommunications.

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