Feature analysis of acoustic emission signals in Time-Frequency representation from partial discharge sources using Self-Organizing Map

2009 ◽  
Author(s):  
Z. Zakaria ◽  
Y.H Md Thayoob ◽  
M.R Samsudin ◽  
P.S Ghosh ◽  
M.L. Chai
Keyword(s):  
Acoustic Emission ◽  
Partial Discharge ◽  
Feature Analysis ◽  
Self Organizing Map ◽  
Time Frequency ◽  
Frequency Representation ◽  
Self Organizing

Postural Control Profiles of Typically Developing Children From 6 to 12 Years old: An Approach Using Self-Organizing Maps

2020 ◽  
Vol 8 (1) ◽  
pp. 52-66 ◽  
Author(s):  
Xavier García-Massó ◽  
Isaac Estevan ◽  
Roberto Izquierdo-Herrera ◽  
Israel Villarrasa-Sapiña ◽  
Luis-Millan Gonzalez
Keyword(s):  
Postural Control ◽  
Visual Information ◽  
High Stability ◽  
Center Of Pressure ◽  
Standing Position ◽  
Self Organizing Map ◽  
Typically Developing ◽  
Typically Developing Children ◽  
Time Frequency ◽  
Self Organizing

The purposes of the present study were a) to establish postural control profiles for individuals 6–12 years of age, b) to analyze the participants’ characteristics (age, sex, weight, height, and physical activity) in those profiles, and c) to analyze the influence of visual information in the profiles found. Two hundred and eight typically developing children aged 6–12 years performed two trials in bipedal standing position with eyes open and closed. Feature extraction involved time, frequency, and sway-density plot variables using signals from the center of pressure. A Self-Organizing Map was used to classify and visualize the values of the participants in all the postural control variables tested. A k-means cluster analysis was applied to generate a small number of postural control profiles. The results determined six postural control profiles; three with participants denoting high stability and three considered as low stability profiles. Age, sex, and height were related to the postural control profiles. Boys were more frequently allocated in high stability clusters than girls, while the other factors yielded unclear difference between high and low stability profiles. The analysis of children’s profiles reflecting postural stability should therefore involve more than one factor including the individuals’ age, sex, and height.

scholarly journals Gearbox fault diagnosis using data fusion based on self-organizing map neural network

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772092347
Author(s):  
Zhang Qiang ◽  
Gu Jieying ◽  
Liu Junming ◽  
Tian Ying ◽  
Zhang Shilei
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Infinite Impulse Response ◽  
Self Organizing Map ◽  
Vibration Displacement ◽  
Gear Fault ◽  
Characteristic Values ◽  
Self Organizing

This article aims to provide an efficient fault diagnosis method for gearbox. A self-organizing map–based fault model is developed to provide effective diagnosis of the faults of gearboxes using the gear signals extracted from gearboxes operating with zero and three different types of faults. The gear signals are collected by vibration and acoustic sensors, and pre-denoised using wavelet denoising and wavelet packet decomposition. The characteristic values are subsequently obtained using fast Fourier transform and infinite impulse response filtering. The results showed of the self-organizing map neural network diagnosis model can effectively diagnose gear fault information with a 95% diagnostic accuracy using four input characteristic values: (1) Y-axis vibration displacement amplitude, (2) Y-axis vibration acceleration amplitude, (3) acoustic emission energy amplitude, and (4) acoustic emission signal peak value. The proposed approach provides a novel method to more accurate diagnosis of gear fault pattern and improvement of working efficiency of mechanical instruments.

Exploring the multiscale changeability of precipitation using the entropy concept and self-organizing maps

2019 ◽  
Vol 11 (3) ◽  
pp. 655-676
Author(s):  
Kiyoumars Roushangar ◽  
Farhad Alizadeh ◽  
Jan Adamowski ◽  
Seyed Mehdi Saghebian
Keyword(s):  
Time Series ◽  
Annual Precipitation ◽  
Optimum Number ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Time Frequency ◽  
Modes Of Variability ◽  
Rain Gauges ◽  
Spatio Temporal ◽  
Self Organizing

Abstract This study utilized a spatio-temporal framework to assess the dispersion and uncertainty of precipitation in Iran. Thirty-one rain gauges with data from 1960 to 2010 were selected in order to apply the entropy concept and study spatio-temporal variability of precipitation. The variability of monthly, seasonal and annual precipitation series was studied using the marginal disorder index (MDI). To investigate the intra-annual and decadal distribution of monthly and annual precipitation values, the apportionment disorder index (ADI) and decadal ADI (DADI) were applied to the time series. The continuous wavelet transform was used to decompose the ADI time series into time-frequency domains. The decomposition of the ADI series into different zones helped to identify the dominant modes of variability and the variation of those modes over time. The results revealed the high disorderliness in the amount of precipitation for different temporal scales based on disorder indices. Based on the DI outcome for all rain gauges, a self-organizing map (SOM) was trained to find the optimum number of clusters (seven) of rain gauges. It was observed from the clustering that there was hydrologic similarity in the clusters apart from the geographic neighborhood.

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