Damage detection of structures with detrended fluctuation and detrended cross-correlation analyses

2017 ◽  
Vol 26 (3) ◽  
pp. 035027 ◽  
Author(s):  
Tzu-Kang Lin ◽  
Haikal Fajri
Keyword(s):  
Damage Detection ◽  
Cross Correlation ◽  
Correlation Analyses ◽  
Detrended Fluctuation

scholarly journals Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies

2019 ◽  
Vol 7 (3) ◽  
pp. 51 ◽  
Author(s):  
Natália Costa ◽  
César Silva ◽  
Paulo Ferreira
Keyword(s):  
Time Scales ◽  
Cross Correlation ◽  
Data Availability ◽  
Fluctuation Analysis ◽  
Financial Assets ◽  
Sliding Windows ◽  
Cross Correlation Analysis ◽  
Short Time ◽  
Detrended Fluctuation ◽  
Great Development

In recent years, increasing attention has been devoted to cryptocurrencies, owing to their great development and valorization. In this study, we propose to analyse four of the major cryptocurrencies, based on their market capitalization and data availability: Bitcoin, Ethereum, Ripple, and Litecoin. We apply detrended fluctuation analysis (the regular one and with a sliding windows approach) and detrended cross-correlation analysis and the respective correlation coefficient. We find that Bitcoin and Ripple seem to behave as efficient financial assets, while Ethereum and Litecoin present some evidence of persistence. When correlating Bitcoin with the other cryptocurrencies under analysis, we find that for short time scales, all the cryptocurrencies have statistically significant correlations with Bitcoin, although Ripple has the highest correlations. For higher time scales, Ripple is the only cryptocurrency with significant correlation.

scholarly journals Binocular cross-correlation analyses of the effects of high-order aberrations on the stereoacuity of eyes with keratoconus

2019 ◽  
Vol 19 (6) ◽  
pp. 12 ◽  
Author(s):  
Sangeetha Metlapally ◽  
Shrikant R. Bharadwaj ◽  
Austin Roorda ◽  
Vinay Kumar Nilagiri ◽  
Tiffanie T. Yu ◽  
...  
Keyword(s):  
Cross Correlation ◽  
High Order ◽  
Correlation Analyses ◽  
High Order Aberrations

scholarly journals Scale Analysis and Correlation Study of Wildfire and the Meteorological Factors That Influence It

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jiazheng Lu ◽  
Tejun Zhou ◽  
Bo Li ◽  
Chuanping Wu
Keyword(s):  
Time Series ◽  
Time Scale ◽  
Detrended Fluctuation Analysis ◽  
Cross Correlation ◽  
Daily Precipitation ◽  
Meteorological Factors ◽  
Fluctuation Analysis ◽  
Average Temperature ◽  
Cross Correlation Analysis ◽  
Detrended Fluctuation

Wildfire is a large-scale complex system. Insight into the mechanism that drives wildfires can be revealed by the distribution of the wildfire over a large time scale, which is one of the important topics in wildfire research. In this study, the scaling properties of four meteorological factors (relative humidity, daily precipitation, daily average temperature, and maximum wind speed) that can affect wildfires (number of wildfires per day) were investigated by using the detrended fluctuation analysis method. The results showed that the time series for these meteorological factors and wildfires have similar power exponents and turning points for the power exponents curve. The five types of time series have a lasting and steady long-range power law correlation over a certain time scale range, where the corresponding exponents were 0.6484, 0.5724, 0.8647, 0.7344, and 0.6734, respectively. They also have a reversible long-range power law correlation beyond a certain time scale, where the corresponding exponents are 0.3862, 0.2218, 0.1372, 0.2621, and 0.2678. The multifractal detrended fluctuation analysis results showed that the wildfire time series were multifractal. The results of the research based on the detrended cross-correlation analysis and the multifractal detrended cross-correlation analysis showed that relative humidity and daily precipitation have a considerable impact on the wildfire time series, while the impacts of daily average temperature and the maximum wind speed are relatively small. This study showed that identifying the factors causing the inherent volatility in the wildfire time series can improve understanding of the dynamic mechanism controlling wildfires and the meteorological parameters. These results can also be used to quantify the correlation between wildfire and the meteorological factors investigated in this study.

Damage Detection Based on the Cross Correlation Function Amplitude Vector and its Application to the ASCE Benchmark Structure

2007 ◽  
Vol 353-358 ◽  
pp. 2317-2320 ◽  
Author(s):  
Zhe Feng Yu ◽  
Zhi Chun Yang
Keyword(s):  
Correlation Function ◽  
Damage Detection ◽  
Structural Damage ◽  
Cross Correlation ◽  
Relative Difference ◽  
Random Excitation ◽  
Cross Correlation Function ◽  
Vibration Responses ◽  
Specific Frequency ◽  
The Cross

A new method for structural damage detection based on the Cross Correlation Function Amplitude Vector (CorV) of the measured vibration responses is presented. Under a stationary random excitation with a specific frequency spectrum, the CorV of the structure only depends on the frequency response function matrix of the structure, so the normalized CorV has a specific shape. Thus the damage can be detected and located with the correlativity and the relative difference between CorVs of the intact and damaged structures. With the benchmark problem sponsored by ASCE Task Group on Structural Health Monitoring, the CorV is proved an effective approach to detecting the damage in structures subject to random excitations.

scholarly journals AR, CR and ARCR modeling for simulations and analyses of karst groundwater quality parameters

2018 ◽  
Vol 79 (1) ◽  
pp. 71-81
Author(s):  
Vesna Ristic-Vakanjac ◽  
Marina Cokorilo-Ilic ◽  
Petar Papic ◽  
Dusan Polomcic ◽  
Radisav Golubovic
Keyword(s):  
Groundwater Quality ◽  
Cross Correlation ◽  
Groundwater Resources ◽  
Extreme Rainfall ◽  
Bacterial Count ◽  
Quality Parameters ◽  
Theoretical Background ◽  
Precipitation Regime ◽  
Correlation Analyses ◽  
Nitrate Concentrations

Although an invisible component of the hydrologic cycle, groundwater generally takes precedence over other water resources in the area of drinking water supply. Among groundwater resources, karst aquifers tend to be rich in sufficiently-accessible amounts of high-quality water. During most of the year, this water requires only disinfection prior to delivery to the end user. However, in many cases extreme rainfall and/or sudden snow melt results in transient turbidity, increase in bacterial count and temporary contamination (e.g. increase in nitrate and phosphate concentrations). To be able to determine the effect of the precipitation regime on various groundwater quality parameters, it is necessary to establish continuous monitoring of the parameter of interest and certain parameters should be observed at least once a day, if not more often (continuously). Such monitoring provides sufficiently long time-series of the considered parameter, so that autocorrelation and cross-correlation analyses can be undertaken and AR, CR and ARCR modeling used for simulations and short-term forecasts. Apart from the theoretical background, the paper presents a case study of the occurrence of nitrates at a karst spring called ?Banja? near the city of Valjevo, Serbia. A ten-year (1991-2000) timeseries of the discharged volume of water was used in the study, as well as nitrate concentrations recorded on a daily basis. In addition, daily precipitation was gauged in the immediate vicinity of the catchment and the rainwater chemically analyzed. The analyses included nitrate concentrations in precipitation. The generated timeseries were used for autocorrelation and cross-correlation analyses of nitrate concentrations in the Banja Spring pool during the entire period of monitoring, as well as in one wet and one dry year. The results are presented for all three cases, based on simulations applying AR, CR and ARCR modeling.

Vibration-based damage detection for bridges by deep convolutional denoising autoencoder

2020 ◽  
pp. 147592172094283 ◽  
Author(s):  
Zhiqiang Shang ◽  
Limin Sun ◽  
Ye Xia ◽  
Wei Zhang
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Correlation Functions ◽  
Structural Damage ◽  
Cross Correlation ◽  
Deep Neural Networks ◽  
Beam Model ◽  
Structural Damage Detection ◽  
Denoising Autoencoder ◽  
Vibration Data

One of the main challenges for structural damage detection using monitoring data is to acquire features that are sensitive to damages but insensitive to noise (e.g. sensor measurement noise) as well as environmental and operational effects (e.g. temperature effect). Inspired by the capabilities of deep learning methods in representation learning, various deep neural networks have been developed to obtain effective damage features from raw vibration data. However, most of the available deep neural networks are supervised, resulting in practical difficulties owing to the lack of damage labels. This article proposes a damage detection strategy based on an unsupervised deep neural network, referred to as deep convolutional denoising autoencoder, which accepts multi-dimensional cross-correlation functions as input. The strategy aims to extract damage features from field measurements of undamaged structures under the influence of noise and temperature uncertainties. In the proposed strategy, cross-correlation functions of vibration data are first calculated as basic features; then deep convolutional denoising autoencoder is developed to reconstruct cross-correlation functions from their noise-corrupted versions to extract desired features; exponentially weighted moving average control charts are finally established for these features to identify minor structural damages. The strategy is evaluated through a numerical simply supported beam model and an experimental continuous beam model. The mechanism of deep convolutional denoising autoencoder to extract damage features is interpreted by visualizing feature maps of convolutional layers in the encoder. It is found that these layers perform rough estimations of modal properties and preserve the damage information as the general trend of these properties in multiple extra frequency bands. The results show that the proposed strategy is competent for structural damage detection under the exposed environment and worth further exploring its capabilities in applications of real bridges.

Диагностика ударных повреждений монолитных и сотовых углепластиков с помощью ультразвуковых волн Лэмба

2021 ◽  
pp. 33-43
Author(s):  
М.В. Бурков ◽  
А.В. Еремин ◽  
А.В. Бяков ◽  
П.С. Любутин ◽  
С.В. Панин
Keyword(s):  
Damage Detection ◽  
Impact Energy ◽  
Cross Correlation ◽  
Lamb Waves ◽  
Impact Damage ◽  
Damage Index ◽  
The Other ◽  
State Testing ◽  
Accurate Data ◽  
Damage Location

The paper presents the results on Lamb waves based technique for impact damage detection and severity identification. The PZT network operates in the round-robin mode changing the actuator and sensor roles of the transducers in order to detect the response of the system in the presence of damage. The monitoring is performed via the analysis of three parameters: change of the amplitude (dA), change of the energy (dP) and cross-correlation (NCC) of the signals in baseline and damaged state. Testing of laminate CFRPs shows that the damage location is estimated within the 5–15 mm error, while the computed Damage index linearly is dependent on the applied impact energy. For honeycomb CFRPs the NCC parameter do not provide accurate results, however, the other parameters allow identification within the 5–20 mm error and reflect accurate data on the severity of the damage.

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