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 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

scholarly journals Nonlinear time series analysis of vibration data from a friction brake: SSA, PCA, and MFDFA

2014 ◽  
Vol 69 ◽  
pp. 90-99 ◽  
Author(s):  
Nikolay K. Vitanov ◽  
Norbert P. Hoffmann ◽  
Boris Wernitz
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Nonlinear Time Series ◽  
Nonlinear Time Series Analysis ◽  
Vibration Data ◽  
Series Analysis ◽  
Friction Brake

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.

scholarly journals Modeling and Simulation of Biogas Production in Full Scale with Time Series Analysis

2021 ◽  
Vol 9 (2) ◽  
pp. 324
Author(s):  
Celina Dittmer ◽  
Johannes Krümpel ◽  
Andreas Lemmer
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Biogas Production ◽  
Solid Substrate ◽  
Full Scale ◽  
Percentage Error ◽  
Measurement Technology ◽  
Series Analysis ◽  
Substrate Supply ◽  
Extensive Evaluation

Future biogas plants must be able to produce biogas according to demand, which requires proactive feeding management. Therefore, the simulation of biogas production depending on the substrate supply is assumed. Most simulation models are based on the complex Anaerobic Digestion Model No. 1 (ADM1). The ADM1 includes a large number of parameters for all biochemical and physicochemical process steps, which have to be carefully adjusted to represent the conditions of a respective full-scale biogas plant. Due to a deficiency of reliable measurement technology and process monitoring, nearly none of these parameters are available for full-scale plants. The present research investigation shows a simulation model, which is based on the principle of time series analysis and uses only historical data of biogas formation and solid substrate supply, without differentiation of individual substrates. The results of an extensive evaluation of the model over 366 simulations with 48-h horizon show a mean absolute percentage error (MAPE) of 14–18%. The evaluation is based on two different digesters and demonstrated that the model is self-learning and automatically adaptable to the respective application, independent of the substrate’s composition.

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
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