This paper is concerned by the modal identification of time-varying mechanical systems. Based on previous works about autoregressive moving average models in vector form (ARMAV) for the modal ...identification of linear time invariant systems, and time-varying autoregressive moving average models (TV-ARMA) for the identification of nonstationary systems, a time-varying ARMAV (TV-ARMAV) model is presented for the multivariate identification of time-varying systems. It results in the identification of not only the time-varying poles of the system but also of its respective time-varying mode shapes. The method is applied on a time-varying structure composed of a beam on which a mass is moving.
•The modal identification of time-varying systems is addressed.•Multivariate autoregressive moving-average models are used for the identification.•The time-varying behavior is handled by the use of basis functions.•All the time-varying modal parameters are identified (poles and mode shapes).•The method is successfully tested on an experimental structure.
Highly public anti-Black violence in the United States may cause widely experienced distress for Black Americans. This study identifies 49 publicized incidents of racial violence and quantifies ...national interest based on Google searches; incidents include police killings of Black individuals, decisions not to indict or convict the officer involved, and hate crime murders. Weekly time series of population mental health are produced for 2012 through 2017 using two sources: 1) Google Trends as national search volume for psychological distress terms and 2) the Behavioral Risk Factor Surveillance System (BRFSS) as average poor mental health days in the past 30 d among Black respondents (mean weekly sample size of 696). Autoregressive moving average (ARMA) models accounted for autocorrelation, monthly unemployment, season and year effects, 52-wk lags, news-related searches for suicide (for Google Trends), and depression prevalence and percent female (for BRFSS). National search interest varied more than 100-fold between racial violence incidents. Black BRFSS respondents reported 0.26 more poor mental health days during weeks with two or more racial incidents relative to none, and 0.13 more days with each log
increase in national interest. Estimates were robust to sensitivity tests, including controlling for monthly number of Black homicide victims and weekly search interest in riots. As expected, racial incidents did not predict average poor mental health days among White BRFSS respondents. Results with national psychological distress from Google Trends were mixed but generally unsupportive of hypotheses. Reducing anti-Black violence may benefit Black Americans' mental health nationally.
In real life, the distribution of the errors during any life testing of products or process does not meet the assumption of normality. Statistical process control (SPC) is defined as the use of ...statistical techniques to control a process or production method. SPC tools and procedures can help to monitor process behavior, discover problems in internal systems, and find solutions for production issues. To identify and remove the variation in different reliability processes and to monitor the reliability of machines where the number of errors follows skewed distributions, we develop control charts to keep the process in control. For such situations, we have modified the existing control charts such as Shewhart control chart, exponentially weighted moving average (EWMA), hybrid exponentially weighted moving average (HEWMA) and extended exponentially weighted moving average (EEWMA) control charts. The current study introduced classical estimator based modified control charts for phase-II monitoring by assuming that the errors occur during the process follow skewed distribution called Beta Lehmann 2 Power function distribution (BL2PFD). The proposal for these control charts is based on the percentile estimator. We have compared all these control charts using Monte Carlo simulation studies and real-life applications to compare the proposed control charts. This study shows that an EEWMA control chart based on PE performs better than Shewhart, EWMA and HEWMA control charts, when the underlying distribution of the errors in process monitoring follows BL2PFD. These findings can be useful for researchers and practitioners in dealing with production errors and optimizing the output.
Wind power prediction is the key technology to the safe dispatch and stable operation of power system with large-scale integration of wind power. In this work, based on the historical data of wind ...power, wind speed and temperature, the autoregressive moving average (ARMA) prediction model and the support vector machine (SVM) prediction model are established, particle swarm optimization (PSO) algorithm is involved for parameter optimization of SVM model. Furthermore, a hybrid PSO-SVM-ARMA prediction model based on ARMA and PSO-SVM model is illustrated for wind power prediction, and the covariance minimization method and PSO are employed to find the optimal weights. Moreover, with the basis of clustering theory, time series are clustered to examine the effective dataset for wind power prediction, and a clustered hybrid PSO-SVM-ARMA (C-PSO-SVM-ARMA) wind power prediction model is prospectively proposed. In case study, different prediction models are carried out and the prediction performance is examined based on different evaluation indices, the C-PSO-SVM-ARMA model shows better performance for wind power prediction with computational efficiency and satisfying precision.
•Models for wind speed prediction at 10-min intervals up to 1h built on time-series wind speed data.•Four different multivariate models for wind speed built based on exogenous variables.•Non-linear ...models built using three data mining algorithms outperform the linear models.•Autoregressive models based on wind direction perform better than other models.
Wind speed forecasting aids in estimating the energy produced from wind farms. The soaring energy demands of the world and minimal availability of conventional energy sources have significantly increased the role of non-conventional sources of energy like solar, wind, etc. Development of models for wind speed forecasting with higher reliability and greater accuracy is the need of the hour. In this paper, models for predicting wind speed at 10-min intervals up to 1h have been built based on linear and non-linear autoregressive moving average models with and without external variables. The autoregressive moving average models based on wind direction and annual trends have been built using data obtained from Sotavento Galicia Plc. and autoregressive moving average models based on wind direction, wind shear and temperature have been built on data obtained from Centre for Wind Energy Technology, Chennai, India. While the parameters of the linear models are obtained using the Gauss–Newton algorithm, the non-linear autoregressive models are developed using three different data mining algorithms. The accuracy of the models has been measured using three performance metrics namely, the Mean Absolute Error, Root Mean Squared Error and Mean Absolute Percentage Error.
Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a ...result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.
Because of the uncertainty and randomness of wind speed, wind power has characteristics such as nonlinearity and multiple frequencies. Accurate prediction of wind power is one effective means of ...improving wind power integration. Because the traditional single model cannot fully characterize the fluctuating characteristics of wind power, scholars have attempted to build other prediction models based on empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD) to tackle this problem. However, the prediction accuracy of these models is affected by modal aliasing and illusive components. Aimed at these defects, this paper proposes a multi-frequency combination prediction model based on variational mode decomposition (VMD). We use a back propagation neural network (BPNN), autoregressive moving average (ARMA) model, and least squares support vector machine (LS-SVM) to predict high, intermediate, and low frequency components, respectively. Based on the predicted values of each component, the BPNN is applied to combine them into a final wind power prediction value. Finally, the prediction performance of the single prediction models (ARMA, BPNN, LS-SVM) and the decomposition prediction models (EMD and EEMD) are used to compare with the proposed VMD model according to the evaluation indices such as average absolute error, mean square error, and root mean square error to validate its feasibility and accuracy. The results show that the prediction accuracy of the proposed VMD model is higher.
Recent advances in sensor technologies and data acquisition systems opened up the era of big data in the field of structural health monitoring (SHM). Data-driven methods based on statistical pattern ...recognition provide outstanding opportunities to implement a long-term SHM strategy, by exploiting measured vibration data. However, their main limitation, due to big data or high-dimensional features, is linked to the complex and time-consuming procedures for feature extraction and/or statistical decision-making. To cope with this issue, in this article we propose a strategy based on autoregressive moving average (ARMA) modeling for feature extraction, and on an innovative hybrid divergence-based method for feature classification. Data relevant to a cable-stayed bridge are accounted for to assess the effectiveness and efficiency of the proposed method. The results show that the offered hybrid divergence-based method, in conjunction with ARMA modeling, succeeds in detecting damage in cases strongly characterized by big data.
•Two novel hybrid wind speed forecasting systems are proposed.•Differ ways are used to extract the linear and nonlinear components of time series.•To reduce the noise in the raw data, a time series ...preprocessing model is applied.•It mends for the defect of low forecast accuracy of alone linear or nonlinear model.•These new systems are tested on nine wind speed datasets of the Penglai wind farm.
Reliable and accurate wind speed forecasting is the basis for the effective development of wind energy. However, wind speed is intermittent, presents nonlinear patterns, and exhibits nonstationary behavior; thus, it is generally difficult to predict it accurately and efficiently using a single linear or nonlinear model. Hence, in this study, two novel hybrid forecasting systems based on the structural characteristics of wind speed are proposed to capture the linear and nonlinear factors hidden in wind speed series. First, a decomposition algorithm is used to eliminate noise from raw data and reconstruct a more reliable wind speed time series. Then, a linear model, which employs the exponential smoothing model or autoregressive moving average model, captures the linear patterns hidden in the time series, and a nonlinear model, which applies the back propagation neural network optimized by the cuckoo search algorithm, extracts the nonlinear patterns hidden in the data. The experimental results using nine datasets show that the proposed model has better prediction accuracy than the comparison models and the root mean square error (RMSE), the mean absolute error (MAE) are respectively less than 0.2139 and 0.125, which provides a scientific basis for power grid dispatch and guarantees the stable operation of the wind power system.
•This study raised a substructural damage detection approach based on ARMAX model and optimal subpattern assignment (OSPA) distance to locate and quantify the damages.•The features of a linear ...dynamic system can be represented by its poles estimated from ARMAX model, transforming the damage detection into multi-target tracking of system poles.•The OSPA distance is used as the damage indicator of a function of the ARMAX model poles in this study and is calculated by Hungarian algorithm.
A novel substructural damage detection approach based on auto-regressive moving average with exogenous inputs (ARMAX) model and optimal subpattern assignment (OSPA) distance is proposed to locate and quantify the damages. Firstly, the shear structure is divided into independent substructures so that damage identification can be performed on each substructure modeling with multi-input multi-output (MIMO) model, which is promising for practical application and distributed structural health monitoring. Compared with the auto-regressive (AR) model, the ARMAX model structure involves not only the outputs but also the inputs and the disturbance dynamics, which help to improve the performance of modeling every substructure and gain the flexibility to handle the disturbance caused by environmental noises even the structural responses contain strong correlations under different excitations. The features of a linear dynamic system can be represented by its poles estimated from ARMAX model, transforming the damage detection into multi-target tracking of system poles. The OSPA distance is used as an innovative damage indicator of a function of the ARMAX model poles in this study, and is calculated by Hungarian algorithm. Experimental verifications were conducted to prove satisfactory damage detection. The application on nonlinear damage identification in a complex three-dimensional reinforced concrete structure shows the great potential of the proposed pole-based OSPA distance in multi-sensor information fusion of structural responses from different directions.
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