Feature Extraction for Damage Detection in Structures Based on Nonlinearity Analysis

2009 ◽  
Vol 413-414 ◽  
pp. 627-634 ◽  
Author(s):  
Zhi Ke Peng ◽  
Z.Q. Lang ◽  
C. Wolters ◽  
S.A. Billings
Keyword(s):  
Damage Detection ◽  
Moving Average ◽  
Nonlinear System Identification ◽  
Output Frequency ◽  
Response Data ◽  
Modelling Method ◽  
Auto Regressive ◽  
Identification Approach ◽  
Engineering Significance ◽  
Measured Response

In the present study, a nonlinear system identification approach known as NARMAX (Nonlinear Auto-Regressive Moving Average with eXogenous Inputs) modelling method and the NOFRF (Nonlinear Output Frequency Response Function) are introduced to detect damage in plate. A set of NOFRF-based damage features is proposed, and the procedure about how to extract the features from the measured response data is presented in detail. An experimental application to the detection of damages in aluminium plates demonstrates the effectiveness and engineering significance of the new damage detection technique.

scholarly journals A Universal Fast Algorithm for Sensitivity-Based Structural Damage Detection

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Q. W. Yang ◽  
J. K. Liu ◽  
C.H. Li ◽  
C.F. Liang
Keyword(s):  
Damage Detection ◽  
Fast Algorithm ◽  
Structural Damage ◽  
Research Area ◽  
Detection Methods ◽  
Structural Damage Detection ◽  
Response Data ◽  
Flexibility Matrix ◽  
New Research ◽  
Measured Response

Structural damage detection using measured response data has emerged as a new research area in civil, mechanical, and aerospace engineering communities in recent years. In this paper, a universal fast algorithm is presented for sensitivity-based structural damage detection, which can quickly improve the calculation accuracy of the existing sensitivity-based technique without any high-order sensitivity analysis or multi-iterations. The key formula of the universal fast algorithm is derived from the stiffness and flexibility matrix spectral decomposition theory. With the introduction of the key formula, the proposed method is able to quickly achieve more accurate results than that obtained by the original sensitivity-based methods, regardless of whether the damage is small or large. Three examples are used to demonstrate the feasibility and superiority of the proposed method. It has been shown that the universal fast algorithm is simple to implement and quickly gains higher accuracy over the existing sensitivity-based damage detection methods.

Structural Nonlinear Damage Detection Based on ARMA-GARCH Model

2012 ◽  
Vol 204-208 ◽  
pp. 2891-2896
Author(s):  
Cong Cheng ◽  
Ling Yu ◽  
Liu Jie Chen
Keyword(s):  
Time Series ◽  
Damage Detection ◽  
Moving Average ◽  
National Laboratory ◽  
Garch Model ◽  
Los Alamos National Laboratory ◽  
Practical Applications ◽  
Moving Average Model ◽  
Auto Regressive ◽  
Story Building

Two economic models, i.e. auto-regressive and moving average model (ARMA) and generalized auto-regressive conditional heteroscedasticity model (GARCH), are adopted to assess the conditions of structures and to detect structural nonlinear damage based on time series analysis in this study. To improve the reliability of the method for nonlinear damage detection, a new damage sensitive feature (DSF) for the ARMA-GARCH model is defined as a ratio of the standard deviation of the variance time series of ARMA-GARCH model residual errors in test condition to ones in reference condition. Compared to the traditional DSF defined as the ratio between the deviations of ARMA-GARCH model residual error in two conditions, the successful outcomes of the new DSF can give obvious explanation for the current states of structures and can detect the nonlinear damage exactly, which enhance the worth of structural health monitoring as well as condition-based maintenance in practical applications. This method is finally verified by a series of experimental data of three-story building structure made in Los Alamos National Laboratory USA.

BLACK BOX MODELLING THE THERMAL BEHAVIOUR OF IHOUSE USING AUTO REGRESSIVE AND MOVING AVERAGE (ARMA) MODEL

2016 ◽  
Vol 78 (6-13) ◽  
Author(s):  
Shamsul Faisal Mohd Hussein ◽  
Hoaison Nguyen ◽  
Shahrum Shah Abdullah ◽  
Yuto Lim ◽  
Yasuo Tan
Keyword(s):  
Control System ◽  
Simulation Model ◽  
Thermal Behaviour ◽  
Moving Average ◽  
Arma Model ◽  
Black Box ◽  
Modelling Method ◽  
Auto Regressive ◽  
Recorded Data ◽  
Better Than

Modelling and simulation of the dynamic thermal behaviour of a building is important to test any proposed thermal comfort control system and strategy in the building. A simulation model can be obtained by using either the white box, grey box or black box modelling method. This research focuses on the usage of auto regressive and moving average (ARMA) model, a type of black box model that represents the dynamic thermal behaviour of iHouse testbed and uses real recorded data from the testbed and limited knowledge regarding the physical characteristics of the testbed. The performance of the ARMA model developed in this research is compared with the performance of House Thermal Simulator, a previously developed model, based on grey box modelling. Results obtained shows that ARMA model works better than House Thermal Simulator in some aspects.  

scholarly journals Auto-Regressive Integrated Moving-Average Machine Learning for Damage Identification of Steel Frames

2021 ◽  
Vol 11 (13) ◽  
pp. 6084
Author(s):  
Yuqing Gao ◽  
Khalid M. Mosalam ◽  
Yueshi Chen ◽  
Wei Wang ◽  
Yiyi Chen
Keyword(s):  
Machine Learning ◽  
Damage Detection ◽  
Structural Damage ◽  
Moving Average ◽  
Confusion Matrix ◽  
Shaking Table ◽  
Parameter Determination ◽  
Local Damage ◽  
Damage Classification ◽  
Auto Regressive

Auto-regressive (AR) time series (TS) models are useful for structural damage detection in vibration-based structural health monitoring (SHM). However, certain limitations, e.g., non-stationarity and subjective feature selection, have reduced its wide-spread use. With increasing trends in machine learning (ML) technologies, automated structural damage recognition is becoming popular and attracting many researchers. In this paper, we combined TS modeling and ML classification to automatically extract damage features and overcome the limitation of non-stationarity. We propose a two-stage framework, namely auto-regressive integrated moving-average machine learning (ARIMA-ML) with modules for pre-processing, model parameter determination, feature extraction, and classification. Based on shaking table tests of a space steel frame, floor acceleration data were collected and labeled according to experimental observations and records. Subsequently, we designed three damage classification tasks for: (1) global damage detection, (2) local damage detection, and (3) local damage pattern recognition. The results from these three tasks indicated the robustness and accuracy of the proposed framework where 97%, 98%, and 80% average segment accuracy were achieved, respectively. The confusion matrix results showed the unbiased model performance even under an imbalanced-class distribution. In summary, the presented study revealed the high potential of the proposed ARIMA-ML framework in vibration-based SHM.

Prognosticating the spread of covid-19 pandemic based on optimal arima estimators

2020 ◽  
Vol 20 ◽  
Author(s):  
Venuka Sandhir ◽  
Vinod Kumar ◽  
Vikash Kumar
Keyword(s):  
South Africa ◽  
Statistical Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Stable Case ◽  
Johns Hopkins University ◽  
Modelling Techniques ◽  
Auto Regressive ◽  
Global Threat

Background: COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19from the explicit data based on optimal ARIMA model estimators. Methods: Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. Results: The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to number of autoregressive terms, d refers to number of times the series has to be differenced before it becomes stationary, and q refers to number of moving average terms. Results obtained from ARIMA model showed significant decrease cases in Australia; stable case for China and rising cases has been observed in other countries. Conclusion: This study tried their best at predicting the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

Towards optimal ATM cash replenishment using time series analysis

2021 ◽  
pp. 1-13
Author(s):  
Muhammad Rafi ◽  
Mohammad Taha Wahab ◽  
Muhammad Bilal Khan ◽  
Hani Raza
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Automatic Teller Machine ◽  
Proposed Model ◽  
Auto Regressive ◽  
Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

Modeling Time Series for Prediction of Thalassemia in Nineveh Governorate

2021 ◽  
Vol 2 (3) ◽  
pp. 120-131
Author(s):  
Shaymaa Riyadh Thanoon
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Monthly Data ◽  
Number Of Patients ◽  
Auto Regressive

The aim of this research is to analyze the time series of Thalassemia cancer cases by making assumptions on the number of cases to formulate the problem to find the best model for predicting the number of patients in Nineveh governorate using (Box and Jenkins) method of analysis based on the monthly data provided by Al Salam Hospital in Nineveh for the period (2014-2018). The results of the analysis showed that the appropriate model of analysis is the Auto-Regressive Integrated Moving Average (ARIMA) (2,1,0) and based on this model the number of people with this disease was predicted for the next two years where the results showed values ​​consistent with the original values which indicates the good quality of the model.

scholarly journals Auto-regressive moving average analysis of linear and discontinuous models of human balance during quiet standing

2014 ◽  
Vol 24 (2) ◽  
pp. 022101 ◽  
Author(s):  
Piotr Kowalczyk ◽  
Salam Nema ◽  
Paul Glendinning ◽  
Ian Loram ◽  
Martin Brown
Keyword(s):  
Moving Average ◽  
Quiet Standing ◽  
Average Analysis ◽  
Human Balance ◽  
Auto Regressive
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