scholarly journals Application of data analysis methods to the results of operation of the marine structure health monitoring system

2020 ◽  
pp. 73-81
Author(s):  
В.А. Коршунов ◽  
О.Н. Петров ◽  
Д.А. Пономарев ◽  
А.А. Родионов
Keyword(s):  
Monitoring System ◽  
Health Monitoring ◽  
Large Data ◽  
Large Data Sets ◽  
Phase Portraits ◽  
Data Sets ◽  
Health Monitoring System ◽  
External Influences ◽  
Marine Structure ◽  
Data Analysis Methods

В работе представлены результаты, анализа и обработки больших массивов данных, получаемых с системы мониторинга состояния МЛСП. Проанализированы зафиксированные внешние воздействия и отклики конструкции с привязкой к временной шкале. Получены коэффициенты корреляции между внешними воздействиями. Построены фазовые портреты внешних воздействий с выраженными аттракторами. Получены коэффициенты корреляции между откликами конструкции. Построены фазовые портреты откликов. Установлены корреляционные зависимости между зафиксированными внешними воздействиями и зарегистрированными откликами конструкции. Проведен кластерный анализ. Установлены связи между кластерами, позволяющие сформулировать гипотезы о более ожидаемых и менее ожидаемых переходах. Определены основные положения для повышения прогнозных характеристик системы мониторинга. In the paper the results of analysis and processing of large data sets obtained from structures of offshore ice-resistant fixed platform health monitoring system are presented. The fixed external influences and responses of the structure are analyzed with reference to the timeline. Coefficients of correlation between external influences are obtained. Phase portraits of external influences with pronounced attractors are constructed. The coefficients of correlation between the responses of the structure are obtained. Phase portraits of responses were constructed. Correlations between the recorded external influences and the registered responses of the structure have been established. Cluster analysis was carried out. The connections between the clusters have been established, which make it possible to formulate hypotheses about more expected and less expected transitions. The main provisions for improving the predictive characteristics of the monitoring system are determined.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

Implementation of Portable Health Monitoring System for Soldiers Using Virtex FPGA Kit

2015 ◽  
Vol 4 (2) ◽  
pp. 5-12
Author(s):  
B. Ponmalathi ◽  
◽  
M. Shenbagapriya ◽  
M. Bharanidharan ◽  
◽  
...  
Keyword(s):  
Monitoring System ◽  
Health Monitoring ◽  
Health Monitoring System
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