Loose Detecting of Tower Crane Bolts Based on Time Series Analysis

This paper deals with the loose detecting of tower crane bolts which is one of the core issues of the tower crane structure health monitoring. Based on the time series model ,we make research on the method of extracting the structure damage factor of tower crane. First, we establish an AR model with the detecting data, use AIC criterion to get AR model order,and then select the residual variance of AR model as the damage sensitive factor. Furthermore, we carry out a single-limb experiment on tower crane and analyze the single-limb experiment state. Good condition and injure state are compared using the above approach. It was found that the approach could effectively judge the healthy and injury states of the tower crane structure with an application value of real-time online damage diagnosis for early warning.

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Research on tower crane structure damage detection based on AR model

2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) ◽

10.1109/iaeac.2017.8054470 ◽

2017 ◽

Author(s):

Shengchun Wang ◽

Ye Zhang ◽

Yan Tian ◽

Mingxiao Dong

Keyword(s):

Damage Detection ◽

Ar Model ◽

Structure Damage ◽

Tower Crane

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Proposal for Tower Crane Productivity Indicators Based on Data Analysis in the Era of Construction 4.0

Buildings ◽

10.3390/buildings11010021 ◽

2021 ◽

Vol 11 (1) ◽

pp. 21

Author(s):

Thomas Danel ◽

Zoubeir Lafhaj ◽

Anand Puppala ◽

Sophie Lienard ◽

Philippe Richard

Keyword(s):

Time Series ◽

Data Analysis ◽

Statistical Analysis ◽

Data Collection ◽

Structural Phase ◽

Sampling Technique ◽

Construction Site ◽

Data Logger ◽

Tower Crane ◽

Productivity Indicators

This article proposes a methodology to measure the productivity of a construction site through the analysis of tower crane data. These data were obtained from a data logger that records a time series of spatial and load data from the lifting machine during the structural phase of a construction project. The first step was data collection, followed by preparation, which consisted of formatting and cleaning the dataset. Then, a visualization step identified which data was the most meaningful for the practitioners. From that, the activity of the tower crane was measured by extracting effective lifting operations using the load signal essentially. Having used such a sampling technique allows statistical analysis on the duration, load, and curvilinear distance of every extracted lifting operation. The build statistical distribution and indicators were finally used to compare construction site productivity.

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AR and ARMA model order selection for time-series modeling with ImageNet classification

Signal Processing ◽

10.1016/j.sigpro.2021.108026 ◽

2021 ◽

Vol 183 ◽

pp. 108026

Author(s):

Jihye Moon ◽

Md Billal Hossain ◽

Ki H. Chon

Keyword(s):

Time Series ◽

Arma Model ◽

Order Selection ◽

Time Series Modeling ◽

Model Order Selection ◽

Model Order ◽

Selection For

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AR model order selection based on bispectral cross-correlation

10.1109/spect.1990.205581 ◽

1990 ◽

Author(s):

J. Noonan ◽

V. Premus ◽

J. Irza

Keyword(s):

Cross Correlation ◽

Ar Model ◽

Order Selection ◽

Model Order Selection ◽

Model Order

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Time Series Modeling on Monthly Data of Tourist Arrivals in Nepal: An Alternative Approach

Nepalese Journal of Statistics ◽

10.3126/njs.v1i0.18816 ◽

2017 ◽

Vol 1 ◽

pp. 41-54 ◽

Cited By ~ 1

Author(s):

Amrit Subedi

Keyword(s):

Time Series ◽

Autoregressive Model ◽

Time Series Data ◽

Polynomial Function ◽

Seasonal Fluctuation ◽

Ar Model ◽

Series Data ◽

Monthly Data ◽

Annual Trend ◽

Alternative Approach

Background: There are various approaches of modeling on time series data. Most of the studies conducted regarding time series data are based on annual trend whereas very few concerned with data having monthly fluctuation. The data of tourist arrivals is an example of time series data with monthly fluctuation which reveals that there is higher number of tourist arrivals in some months/seasons whereas others have less number. Starting from January, it makes a complete cycle in every 12 months with 3 bends indicating that it can be captured by biquadratic function.Objective: To provide an alternative approach of modeling i.e. combination of Autoregressive model with polynomial (biquadratic) function on time series data with monthly/seasonal fluctuation and compare its adequacy with widely used cyclic autoregressive model i.e. AR (12).Materials and Methods: This study is based on monthly data of tourist arrivals in Nepal. Firstly, usual time series model AR (12) has been adopted and an alternative approach of modeling has been attempted combining AR and biquadratic function. The first part of the model i.e. AR represents annual trend whereas biquadratic part does for monthly fluctuation.Results: The fitted cyclic autoregressive model on monthly data of tourist arrivals is Est. Ym = 3614.33 + 0.9509Ym-12, (R2=0.80); Est. Ym indicates predicted tourist arrivals for mth month and Ym-12 indicates observed tourist arrivals in (m-12)th month and the combined model of AR and biquadratic function is Est. Yt(m) = -46464.6 + 1.000Yt-1 + 52911.56m - 17177m2 + 2043.95m3 - 79.43m4, (R2=0.78); Est. Yt(m) indicates predicted tourist arrivals for mth month of tth year and Yt-1 indicates average tourist arrivals in (t-1)th year. The AR model combined with polynomial function reveals normal and homoscedastic residuals more accurately compared to first one.Conclusion: The use of polynomial function combined with autoregressive model can be useful for time series data having seasonal fluctuation. It can be an alternative approach for picking up a good model for such type of data. Nepalese Journal of Statistics, 2017, Vol. 1, 41-54

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Combined risk prediction in the water environment based on an MS–AR model and Copula theory

Water Science & Technology ◽

10.2166/wst.2013.065 ◽

2013 ◽

Vol 67 (9) ◽

pp. 1967-1975 ◽

Cited By ~ 5

Author(s):

Niu Jun-yi ◽

Huang Hu ◽

Chen Na

Keyword(s):

Water Quality ◽

Time Series ◽

Monte Carlo ◽

Risk Prediction ◽

Water Environment ◽

Ar Model ◽

Strong Positive Correlation ◽

State Of Health ◽

Copula Theory ◽

Random Simulation

Making a quantitative prediction on the combined risk of the water body is helpful for the objective evaluation of the water environment system's state of health, and also has important results for the water environment system's safety management. In this paper, the Markov status switching theory (Markov Switching, MS), Monte Carlo method (Monte Carlo, MC) and Copula theory were used together, to establish a method for the water environment system's combined risk assessment. This method firstly using MS theory established the water quality time series' autoregression model (MS–AR); then the MS–AR model and MC method were used to carry out random simulation on the water quality time series; finally, multi-dimensional joint distribution among random simulation results were established by Copula function, and this distribution utilized to make a quantitative analysis of the water environment system's combined risk. By means of the above combined risk analysis model, the combined risk prediction and correlation analysis of the water quality of the Guohe River bridge section were carried out. The results showed that the total phosphorus (TP) and 5-day biochemical oxygen demand (BOD5) had an important effect on the Guohe River water environment's state of health, and there was a strong positive correlation between TP and BOD5.

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Research for Structural Damage Identification Method Based on Stable Time Series and Principal Component Analysis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.578-579.1020 ◽

2014 ◽

Vol 578-579 ◽

pp. 1020-1023

Author(s):

Jing Zhou Lu ◽

Jia Chen Wang ◽

Xu Zhu

Keyword(s):

Principal Component Analysis ◽

Time Series ◽

Damage Detection ◽

Structural Damage ◽

Computing Time ◽

Principal Component ◽

Component Analysis ◽

Identification Accuracy ◽

Frame Structure ◽

Ar Model

In this paper, we introduce a set of techniques for time series analysis based on principal component analysis (PCA). Firstly, the autoregressive (AR) model is established using acceleration response data, and the root mean squared error (RMSE) of AR model is calculated based on PCA. Then a new damage sensitive feature (DSF) based on the AR coefficients is presented. To test the efficacy of the damage detection and localization methodologies, the algorithm has been tested on the analytical and experimental results of a three-story frame structure model of the Los Alamos National Laboratory. The result of the damage detection indicates that the algorithm is able to identify and localize minor to severe damage as defined for the structure. It shows that the suggested method can lead to less amount of computing time, high suitability and identification accuracy.

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Multivariate AR Model Order Estimation with Unknown Process Order

Advances in Informatics - Lecture Notes in Computer Science ◽

10.1007/11573036_75 ◽

2005 ◽

pp. 787-797 ◽

Cited By ~ 1

Author(s):

Stylianos Sp. Pappas ◽

Assimakis K. Leros ◽

Sokratis K. Katsikas

Keyword(s):

Ar Model ◽

Model Order ◽

Order Estimation ◽

Model Order Estimation

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Experimental Verification of the Statistical Time-Series Methods for Diagnosing Wind Turbine Blades Damage

International Journal of Structural Stability and Dynamics ◽

10.1142/s021945541940008x ◽

2018 ◽

Vol 19 (01) ◽

pp. 1940008 ◽

Cited By ~ 1

Author(s):

Hesheng Tang ◽

Suqi Ling ◽

Chunfeng Wan ◽

Songtao Xue

Keyword(s):

Time Series ◽

Wind Turbine ◽

Experimental Verification ◽

Turbine Blades ◽

Ar Model ◽

Wind Turbine Blades ◽

Statistical Decision ◽

Vibration Data ◽

The Status ◽

Statistical Time

This paper presents an experimental verification of the statistical time-series methods, which utilize adapted frequency response ratio (FRR), autoregressive (AR) model parameter and AR model residual as performance characteristics, for diagnosing the damage of wind turbine blades. Specifically, the statistical decision-making techniques are used to identify the status patterns from turbine vibration data. For experiments, a small-size, laboratory-used operating wind turbine structure is used. The performance of each method in diagnosing damages simulated by saw cut in three critical positions in the blade are assessed and compared. The experimental results show that these methods yielded a promising damage diagnosis capability in the condition monitoring of wind turbine.

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