Damage identification using time series analysis and sparse regularization

2020 ◽  
Vol 27 (9) ◽  
Author(s):  
Hongping Zhu ◽  
Hong Yu ◽  
Fei Gao ◽  
Shun Weng ◽  
Yuan Sun ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Damage Identification ◽  
Sparse Regularization ◽  
Series Analysis

Damage identification of a full-scale five-girder bridge using time-series analysis of vibration data

2016 ◽  
Vol 115 ◽  
pp. 129-139 ◽  
Author(s):  
Reza V. Farahani ◽  
Dayakar Penumadu
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Damage Identification ◽  
Full Scale ◽  
Vibration Data ◽  
Series Analysis ◽  
Girder Bridge

Damage Identification of Simply Supported Beam Bridge Based on Time Series Analysis

2012 ◽  
Vol 236-237 ◽  
pp. 617-621
Author(s):  
Han Bing Liu ◽  
Yan Jun Song ◽  
Guo Jin Tan ◽  
Yan Yi Sun
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Dynamic Response ◽  
Time Domain ◽  
Damage Identification ◽  
Simply Supported Beam ◽  
Simply Supported ◽  
Series Analysis ◽  
Wide Range ◽  
Beam Bridge

Presently, the study on damage identification of bridges is very popular and it has a wide range of applications. Also the related theory and technology are constantly developing and mature. The researches based on the dynamic response of bridge in frequency domain is more, but the dynamics theory is complex and difficult for the engineering personnel to grasp. On the opposite, although the damage identification method based on the dynamic response of bridge in time domain is easy to understand, it is difficulty for applications. The Auto Regressive Moving Average model (ARMA) of time series analysis can be used to settle this problem. It is a not very abstruse theory and it is already apply for the system identification of some Structures. In this paper, we use time series analysis for the damage identification of simply supported beam bridge combined with its own dynamic response in time domain.

Nonlinear Time Series Analysis

2003 ◽  
Author(s):  
Holger Kantz ◽  
Thomas Schreiber
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Nonlinear Time Series ◽  
Nonlinear Time Series Analysis ◽  
Series Analysis

Review of Time Series Analysis: Regression Techniques (2nd ed.).

1991 ◽  
Vol 36 (4) ◽  
pp. 349-349
Author(s):  
No authorship indicated
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Series Analysis ◽  
Regression Techniques

Detecting the "hot hand": A time series analysis of basketball

2001 ◽  
Author(s):  
Yanlong Sun ◽  
Ryan D. Tweney
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Series Analysis ◽  
Hot Hand

Dynamical Peculiarities of Concatenational Changes in Functional Cardiovascular Parameters

2018 ◽  
Vol 3 (82) ◽  
Author(s):  
Eurelija Venskaitytė ◽  
Jonas Poderys ◽  
Tadas Česnaitis
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Functional State ◽  
The Body ◽  
Functional Changes ◽  
High Intensity Training ◽  
Series Analysis ◽  
Greco Roman ◽  
St Segment ◽  
Standard Lead

Research  background  and  hypothesis.  Traditional  time  series  analysis  techniques,  which  are  also  used  for the analysis of cardiovascular signals, do not reveal the relationship between the  changes in the indices recorded associated with the multiscale and chaotic structure of the tested object, which allows establishing short-and long-term structural and functional changes.Research aim was to reveal the dynamical peculiarities of interactions of cardiovascular system indices while evaluating the functional state of track-and-field athletes and Greco-Roman wrestlers.Research methods. Twenty two subjects participated in the study, their average age of 23.5 ± 1.7 years. During the study standard 12 lead electrocardiograms (ECG) were recorded. The following ECG parameters were used in the study: duration of RR interval taken from the II standard lead, duration of QRS complex, duration of JT interval and amplitude of ST segment taken from the V standard lead.Research  results.  Significant  differences  were  found  between  inter-parametric  connections  of  ST  segment amplitude and JT interval duration at the pre and post-training testing. Observed changes at different hierarchical levels of the body systems revealed inadequate cardiac metabolic processes, leading to changes in the metabolic rate of the myocardium and reflected in the dynamics of all investigated interactions.Discussion and conclusions. It has been found that peculiarities of the interactions of ECG indices interactions show the exposure of the  functional changes in the body at the onset of the workload. The alterations of the functional state of the body and the signs of fatigue, after athletes performed two high intensity training sessions per day, can be assessed using the approach of the evaluation of interactions between functional variables. Therefore the evaluation of the interactions of physiological signals by using time series analysis methods is suitable for the observation of these processes and the functional state of the body.Keywords: electrocardiogram, time series, functional state.

The Contribution of Foreign Direct Investment for Economic Growth of Ethiopia: Time Series Analysis

2018 ◽  
Vol 9 (02) ◽  
Author(s):  
Addissie Melak
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Foreign Direct Investment ◽  
Time Series Analysis ◽  
Direct Investment ◽  
Policy Framework ◽  
Annual Growth Rate ◽  
African Countries ◽  
Series Analysis ◽  
Integration Test

Economic growth of countries is one of the fundamental questions in economics. Most African countries are opening their economies for welcoming of foreign investors. As such Ethiopia, like many African countries took measures to attract and improve foreign direct investment. The purpose of this study is to examine the contribution of foreign direct investment (FDI) for economic growth of Ethiopia over the period of 1981-2013. The study shows an overview of Ethiopian economy and investment environment by the help of descriptive and econometric methods of analysis to establish empirical investigation for the contribution of FDI on Ethiopian economy. OLS method of time series analysis is employed to analyse the data. The stationary of the variables have been checked by using Augmented Dickey Fuller (ADF) Unit Root test and hence they are stationery at first difference. The co- integration test also shows that there is a long run relationship between the dependent and independent variables. Accordingly, the finding of the study shows that FDI, GDP per capita, exchange rate, total investment as percentage of GDP, inflow of FDI stock, trade as percentage of GDP, annual growth rate of GDP and liberalization of the economy have positive impact on Ethiopian GDP. Whereas Gross fixed domestic investment, inflows of FDI and Gross capital formation influence economic growth of Ethiopia negatively. This finding suggests that there should be better policy framework to attract and improve the volume of FDI through creating conducive environment for investment.

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