Spatio‐temporal fault localization for nonlinear spatially distributed processes: A spatial mapping filter‐based framework

2021 ◽  
Author(s):  
Yun Feng ◽  
Yaonan Wang ◽  
Jun‐Wei Wang ◽  
Han‐Xiong Li ◽  
Steven X. Ding
Keyword(s):  
Fault Localization ◽  
Spatial Mapping ◽  
Distributed Processes ◽  
Spatially Distributed ◽  
Spatio Temporal

scholarly journals Spatio-temporal Analysis of Maximum and Minimum Temperature in Bangladesh

2015 ◽  
Vol 7 (2) ◽  
pp. 73-77 ◽  
Author(s):  
MN Uddin ◽  
MSA Mondal ◽  
NMR Nasher
Keyword(s):  
Spatial Distribution ◽  
Minimum Temperature ◽  
Statistical Significance ◽  
Temporal Analysis ◽  
Temperature Data ◽  
Spatially Distributed ◽  
Spatio Temporal ◽  
Gis Environment ◽  
Maximum Temperatures ◽  
Kendall Method

The analysis of annual mean maximum and annual mean minimum temperature data are studied in GIS environment, obtained from 34 meteorological stations scattered throughout the Bangladesh from 1948 to 2013. IDW method was used for the spatial distribution of temperature over the study area, using ArcGIS 10.2 software. Possible trends in the spatially distributed temperature data were examined, using the non-parametric Mann-Kendall method with statistical significance, and the magnitudes of available trends were determined using Sen’s method in ArcMap depiction. The findings of the study show positive trends in annual mean maximum temperatures with 90%, 95%, 99% and 99.9% significance levels.DOI: http://dx.doi.org/10.3329/jesnr.v7i2.22210 J. Environ. Sci. & Natural Resources, 7(2): 73-77 2014

Acquirement and Interpretation of Spatially Distributed Impulsive Acceleration Signals in a Flexible Shaft-Propeller System

2012 ◽  
Author(s):  
Ioannis T. Georgiou
Keyword(s):  
Distributed Information ◽  
Spatially Distributed ◽  
Experimental Time Series ◽  
Impulsive Response ◽  
Impulsive Dynamics ◽  
Spatio Temporal ◽  
Pod Analysis ◽  
Proper Orthogonal ◽  
Acceleration Signals

This work reports on the spatio-temporal characterization of collocated ensembles of experimental time series of the impulsive dynamics in a complex flexible structural-machinery system composed of a shaft supporting at its free end a three-bladed boat propeller. A few state-of-the-art piezoelectric accelerometers sample simultaneously the impulsive response of the system as a function of the location of a modal hammer force applied sequentially over a global interrogation curve. The typical ensemble of collocated signals is subject to a systematic coherence analysis by advanced tools based on the theory of Proper Orthogonal Decomposition (POD) transforms. It turns out that three different collocated ensembles of acceleration signals, collected at the blades at symmetric locations, are very coherent and robust as distributed information over the space-time domain. The characteristics of coherence of the collocated databases are studied in depth by presenting a detailed POD analysis at the system and subsystems levels. The main result is the fact that the POD modes of collocated acceleration databases that exploit structural symmetries seem to provide reliable means to detect sharply differences-due to damage and perhaps design fault-in the subsystem impulsive response.

How well do ERA5/WRF generated wind fields reflect the seasonal variability of the spatio-temporal characteristics of wind field, tested for North-Atlantic conditions

2021 ◽  
Author(s):  
Hans Georg Beyer ◽  
Tourid Poulsen
Keyword(s):  
Spectral Characteristics ◽  
Reanalysis Data ◽  
Spatial Inhomogeneity ◽  
Spectral Structure ◽  
Wind Fields ◽  
Temporal Characteristics ◽  
Wind Speeds ◽  
Weather Model ◽  
Spatially Distributed ◽  
Spatio Temporal

<p>Spatial and temporal characteristics of simulated wind fields - stemming from the high-resolution weather model WRF with boundary conditions from ERA5 reanalysis data had been validated against the respective data stemming from measurements regarding annual characteristics as reported by [1]. As one result, the tendency of the WRF sets showed some overestimation of the coherency and underestimation of the power spectral density (PSD).</p><p>Here, this investigation is deepened to look on the capability of the modelled data to reflect the variability of the PSD and coherencies of the wind speed fluctuations on a monthly and seasonal (three- monthly) scale.</p><p>The intra annual variation of the PSD and the coherence functions are well captured by the WRF-generated wind speeds.  No seasonal dependency can be detected for the underestimation of the spectra from the modelled data. The shape can well be modelled by the approach of [Larsén et al., 2013].  Concerning the coherences, the tendency of an overestimation as detected in the analysis of annual sets, shows up in the seasonal scale in similar magnitude, reflecting a systematic shortcoming of the simulated sets to reflect the spatial inhomogeneity of the field.</p><p>[1] Poulsen, T, Beyer, H.G., Cross spectral characteristics of  modelled and measured sets of spatially distributed wind in the Faroe Islands, poster presentation  EGU 2020 (2020)</p><p>[2] Larsén, X., Vincent, C., and Larsen, S. (2013). Spectral structure of mesoscale wind over the water. Quarterly Journal of the Royal Meteorological Society, 139:685–700. (2013)</p>

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