Autoregressive Exogenous Model Research Articles

Corrosion morphology prediction of civil infrastructure using a physics-constrained machine learning method

Corrosion morphology prediction for civil infrastructure using physics-based simulation is computationally challenging due to the coupled multi-physics simulations involved in long-term corrosion prognostics. Machine learning (ML)-based surrogate modeling provides a promising way of overcoming this challenge. This paper presents a physics-constrained ML method for surrogate modeling of a high-fidelity multi-physics pitting corrosion simulation, which is solved using a phase-field method. The proposed surrogate model consists of a convolutional variational autoencoder to reduce the dimension of pitting corrosion shape images and a Bayesian multi-layer perceptron network (also known as the Bayesian Latent Space Time Evolution Network) to model the evolution of the corrosion pit morphology over time. To account for the fact that corrosion damage without repair is an irreversible process, a physics constraint is added to the surrogate model to ensure that the corrosion rate is strictly negative. The proposed physics-constrained surrogate modeling method is compared with a purely data-driven surrogate modeling method which integrates a convolutional neural network autoencoder with a Gaussian process regression (GPR) model-based nonlinear autoregressive exogenous model. The results show that the proposed physics-constrained surrogate model is significantly more accurate than the purely data-driven GPR-based surrogate model.

Intelligent Analog Beam Selection and Beamspace Channel Tracking in THz Massive MIMO With Lens Antenna Array

Beamspace multiple-input-multiple-output (MIMO) as a green technology can efficiently substitute for the conventional massive MIMO, provided that the beamspace channel is acquired precisely. The prior efforts in this area of study, especially the learning-driven ones, however, indicate remarkable performance losses owing to a lack of generalization. In this paper, we propose a modified non-linear auto-regressive exogenous (NARX) model for tracking and predicting the beamspace channel over the sequences of time. Benefiting from bounded generalization error, fast convergence, limited prediction variance, and negligible performance loss, the proposed scheme achieves up to 15% spectral efficiency (SE) gain over its counterparts. We further improve this performance by means of an ensemble learning technique for simultaneously training multiple NARX modules in parallel, thus leading to a 23% SE gain. Relying on the predicted beamspace channel, we propose a beamspace analog beam selection technique through fine-tuning the architecture of a pre-trained off-the-shelf GoogleNet, which brings up to 21% SE gain over similar baselines. With the aid of an ensemble learning technique, it is further indicated numerically that up to 34% SE improvement can be achieved, as compared to the counterparts.

Design of Nonlinear Marine Predator Heuristics for Hammerstein Autoregressive Exogenous System Identification with Key-Term Separation

Swarm-based metaheuristics have shown significant progress in solving different complex optimization problems, including the parameter identification of linear, as well as nonlinear, systems. Nonlinear systems are inherently stiff and difficult to optimize and, thus, require special attention to effectively estimate their parameters. This study investigates the parameter identification of an input nonlinear autoregressive exogenous (IN-ARX) model through swarm intelligence knacks of the nonlinear marine predators’ algorithm (NMPA). A detailed comparative analysis of the NMPA with other recently introduced metaheuristics, such as Aquila optimizer, prairie dog optimization, reptile search algorithm, sine cosine algorithm, and whale optimization algorithm, established the superiority of the proposed scheme in terms of accurate, robust, and convergent performances for different noise and generation variations. The statistics generated through multiple autonomous executions represent box and whisker plots, along with the Wilcoxon rank-sum test, further confirming the reliability and stability of the NMPA for parameter estimation of IN-ARX systems.

NARX-Elman Based Mach Number Prediction and Model Migration of Wind Tunnel Conditions

Mach number, as an important index to judge the system performance in wind tunnel tests, its stability determines the quality of the flow field of this wind tunnel and needs to be controlled precisely. Due to the complex process in the wind tunnel test, it is difficult to ensure the smooth operation of the Mach number by the traditional control strategy, therefore, a Mach number prediction model based on a nonlinear autoregressive exogenous model (NARX)-Elman is proposed in this paper. Firstly, the NARX model is adopted as the basic framework of this model, the false nearest neighbor (FNN) is used for model order solving, and the Elman network is used for dynamic nonlinear fitting of the model. Secondly, considering the wind tunnel system with multiple conditions and the high cost of conducting complete data experiments, a new model migration method, the input-output slope/bias correction-genetic algorithm (IOSBC-GA), is proposed, which uses IOSBC method as the basic framework to construct the migration model based on the historical condition model and a small amount of data of the new condition, and uses GA to find the slope of deviation in the new model and correct the input-output relationship between the old and new conditions, so as to establish the model for the new condition. By comparing the model respectively with the traditional algorithm and the model built without migration, the root mean square error (RMSE) and the maximum deviation (MD) of this model are less than 0.001, indicating that the model has high prediction accuracy.

Model predictive control with exogenous auto-regressive model to improve performance in the CO2 removal

Model predictive control (MPC) is used in the CO2 removal process in the Subang field to improve its control performance. MPC is used to maintain the CO2 concentration at the sweet gas output by controlling the feed gas pressure (PIC-1101), makeup water flow rate (FIC-1102), and amine flow rate (FIC-1103). The empirical model applied to MPC to represent the process model is the auto-regressive exogenous (ARX) model. The ARX model is compared with the first order plus dead time (FOPDT) model based on the root mean square error (RMSE) between the model and the actual process, then MPC parameters are tuned which include sampling time (T), prediction horizon (P) and control horizon (M) to control for the three variables. Improved control performance is measured based on the integral square error (ISE). The results show that the ARX model is the best model for the CO2 removal process with an RMSE value of 35%-91% smaller than the FOPDT model. The optimal control parameters Prediction Horizon (P), Control Horizon (M) and Sampling Time (T) in the CO2 removal process are 75, 25 and 1 on PIC-1101, 25, 10 and 1 on FIC-1102, and 30, 25 and 1 on FIC-1103. The MPC-ARX (MPC using ARX model) can improve the control performance of 33% in the servo control and 6-56% on the regulatory control. However, not all of them showed an increase in control performance improvement from previous studies even though they had used the best model (ARX). This is due to the MPC parameter setting that is not yet appropriate, so it needs to be retuning.

Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model

In this article, a chaotic computing paradigm is investigated for the parameter estimation of the autoregressive exogenous (ARX) model by exploiting the optimization knacks of an improved chaotic grey wolf optimizer (ICGWO). The identification problem is formulated by defining a mean square error-based fitness function between true and estimated responses of the ARX system. The decision parameters of the ARX model are calculated by ICGWO for various populations, generations, and noise levels. The comparative performance analyses with standard counterparts indicate the worth of the ICGWO for ARX model identification, while the statistical analyses endorse the efficacy of the proposed chaotic scheme in terms of accuracy, robustness, and reliability.

Development of digital twin for PMDC motor control loop

Recent developments in the digital domain can reduce the resources needed for efficient plant control. The growth of cloud computing and big data analysis has paved the way for using digital domain as processing units. Metaverse is the animated version of the physical realm in digital domain. The control mechanism and dynamics of the real-world data cannot be manually fed to the digital domain. The real plant can change its dynamics during its operation which the previously modeled mathematical model cannot address. So, the model for the system needs to be developed by its own and it need to be adaptable. This can be done by implementing the digital twin. In this work, a nonlinear Auto Regressive Exogenous model captures the dynamics of a PMDC motor. A model predictive approach is used to control the PMDC motor, which uses updated model to predict the response and generate the desired control input.

Wear prediction of 3D-printed acrylonitrile butadiene styrene-carbon nanotube nanocomposites at elevated temperatures

Abstract In this study, multi-wall carbon nanotube (MWCNT) reinforced acrylonitrile butadiene styrene (ABS) nanocomposite filaments are produced. Filaments are examined through thermogravimetric analysis (TGA) and definitive scanning calorimetry (DSC) analysis. Produced nanocomposite filaments are used in the fused deposition modeling (FDM) process to manufacture parts. Wear tests are conducted on 3D-printed parts using wear test apparatus with an attached heating module under different ambient temperatures. Hence, the influence of CNT reinforcement, along with different FDM process parameters and varying test conditions on the wear behavior of 3D-printed ABS-CNT parts, are examined. Worn surfaces of the specimens are examined by scanning electron microscopy (SEM). Nonlinear autoregressive exogenous (NARX) models are proposed for the prediction of the wear behavior of 3D-printed ABS-CNT nanocomposites. While wear rate is taken as output, ambient temperature and amount of nanofiller are accounted as input parameters along with the variation of coefficient of friction (COF) which is obtained from measured frictional force and three input-one output model structure is proposed for NARX. The use of multiple input-single output (MISO) model structure and examining the wear behavior of 3D-printed ABS-CNT samples under different wear test conditions with different FDM process parameters are the novelties in this work.

Time-series clustering and forecasting household electricity demand using smart meter data

This study forecasts electricity consumption in a smart grid environment. We present a bottom-up prediction method using a combination of forecasting values based on time-series clustering using advanced metering infrastructure (AMI) data, one of the core smart grid technologies. Remote data metering every 15 min to 1 h is possible with real-time communication on power generation information, consumption, and AMI development. Hence, its prediction is more challenging due to the large variation of each household’s electricity. These issues were solved by time-series clustering methods using Euclidean distances and Dynamic Time Warping distance. The auto-regressive integrated moving average (ARIMA), ARIMA exogenous (ARIMAX), double seasonal Holt–Winters (DSHW), trigonometric, Box–Cox transform, autoregressive moving average errors, trend and seasonal components (TBATS), neural network nonlinear autoregressive (NNAR), and nonlinear autoregressive exogenous (NARX) models were used for demand forecasting based on clustering. The result showed that the time-series clustering method performed better than that using the total amount of electricity demand regarding the mean absolute percentage error and root mean squared error.Hence, various exogenous variables were considered to improve model accuracy. The model considering exogenous variables—cooling degree day, humidity, insolation, indicator variables, and generation power consumption performed better than that without exogenous variables.

NT-Com: A combined machine learning model for picking up first arrival

We present a combined model named NT-Com that could predict the noise of observation site in China and effectively pick up first arrivals with very high accuracy. Based on a nonlinear autoregressive exogenous model (NARX) known as a variant of recurrent neural network and classification-regression trees (CART), NT-Com picks first arrivals by detecting abrupt points in time series. Several experiments were performed to check the fitting degree and prediction accuracy of NARX on noise signals, and experiments on CART were made to measure the accuracy of first break picking. The experimental results of NT-Com are close to those of experts and better than the traditional automatic detection algorithms. Moreover, the accuracy of NT-Com can reach 96%. The time error of the P-wave first arrival pickup can be shortened to 0.5 s. Because various first arrival patterns are learned during the training process, they can be effectively identified, which leads to a much lower probability of false triggers than traditional automatic detection algorithms. Trained and tested by all seismic data in China Earthquake networks from 2009–2019, the research work finds out a solution for picking up first arrivals by combining a simple structure of the neural networks and trees, which can be applied in China with its strong generalization ability and robustness.

Detection of Wheel Polygonization Based on Wayside Monitoring and Artificial Intelligence.

This research presents an approach based on artificial intelligence techniques for wheel polygonization detection. The proposed methodology is tested with dynamic responses induced on the track by passing a Laagrss-type rail vehicle. The dynamic response is attained considering the application of a train-track interaction model that simulates the passage of the train over a set of accelerometers installed on the rail and sleepers. This study, which considers an unsupervised methodology, aims to compare the performance of two feature extraction techniques, namely the Autoregressive Exogenous (ARX) model and Continuous Wavelets Transform (CWT). The extracted features are then submitted to data normalization considering the Principal Component Analysis (PCA) applied to suppress environmental and operational effects. Next to data normalization, data fusion using Mahalanobis distance is performed to enhance the sensitivity to the recognition of defective wheels. Finally, an outlier analysis is employed to distinguish a healthy wheel from a defective one. Moreover, sensitivity analysis is performed to analyze the influence of the number of sensors and their location on the accuracy of the wheel defect detection system.

Damping evaluation of an eight-story steel building with nonlinear oil damper under strong earthquakes

Fluid viscous damper has been applied widely for energy dissipation and shock absorption in dynamic structural systems, specifically the oil damper. Nevertheless, there's an issue of precisely incorporating shaft bearings and pressure sealings for oil dampers, which restricts its wide application in new or existing structures. A type of nonlinear oil damper with viscoelastic polymer packing soft links is posed and investigated to allow for easy production. To explore the effectivity and actual performance of the nonlinear oil dampers, vibration monitoring system has been instrumented both on the dampers and the building since it was built in 2003, which accumulated huge amount of precious data. A Bayesian based order selection of auto-regressive exogenous model is exploited to the recorded earthquake responses to extract dynamic properties of the passively-controlled 8-story steel building. Thus, damping characteristics of the whole structure can be identified and analyzed. Besides, an analytical model for the nonlinear oil damper is investigated and estimated by particle filter improved with Metropolis-Hastings algorithm, which can be incorporated into the whole structure model for model updating, reliability as well as response prediction. Furthermore, the calculation formulas of the additional effective damping ratio provided by oil damper are deduced based on above analysis using modal strain energy, and damping ratio of the main steel frame structure are also estimated. Taking a set of actual ground motion monitoring data as an example, the proposed method is then exploited to estimate the effective damping ratio of oil damper for each floor.

Development of an artificial intelligence-based framework for biogas generation from a micro anaerobic digestion plant

Despite the advantages of the Anaerobic Digestion (AD) technology for organic waste management, low system performance in biogas production negatively affects the wide spread of this technology. This paper develops a new artificial intelligence-based framework to predict and optimise the biogas generated from a micro-AD plant. The framework comprises some main steps including data collection and imputation, recurrent neural network/ Non-Linear Autoregressive Exogenous (NARX) model, shuffled frog leaping algorithm (SFLA) optimisation model and sensitivity analysis. The suggested framework was demonstrated by its application on a real micro-AD plant in London. The NARX model was developed for predicting yielded biogas based on the feeding data over preceding days in which their lag times were fine-tuned using the SFLA. The optimal daily feeding pattern to obtain maximum biogas generation was determined using the SFLA. The results show that the developed framework can improve the productivity of biogas in optimal operation strategy by 43 % compared to business as usual and the average biogas produced can raise from 3.26 to 4.34 m3/day. The optimal feeding pattern during a four-day cycle is to feed over the last two days and thereby reducing the operational costs related to the labour for feeding the plant in the first two days. The results of the sensitivity analysis show the optimised biogas generation is strongly influenced by the content of oats and catering waste as well as the optimal allocated day for adding feed to the main digester compared to other feed variables e.g., added water and soaked liner.

A New Nonlinear Autoregressive Exogenous (NARX)-based Intra-spinal Stimulation Approach to Decode Brain Electrical Activity for Restoration of Leg Movement in Spinally-injured Rabbits.

This study aimed at investigating the stimulation by intra-spinal signals decoded from electrocorticography (ECoG) assessments to restore the movements of the leg in an animal model of spinal cord injury (SCI). The present work is comprised of three steps. First, ECoG signals and the associated leg joint changes (hip, knee, and ankle) in sedated healthy rabbits were recorded in different trials. Second, an appropriate set of intra-spinal electric stimuli was discovered to restore natural leg movements, using the three leg joint movements under a fuzzy-controlled strategy in spinally-injured rabbits under anesthesia. Third, a nonlinear autoregressive exogenous (NARX) neural network model was developed to produce appropriate intra-spinal stimulation developed from decoded ECoG information. The model was able to correlate the ECoG signal data to the intra-spinal stimulation data and finally, induced desired leg movements. In this study, leg movements were also developed from offline ECoG signals (deciphered from rabbits that were not injured) as well as online ECoG data (extracted from the same rabbit after SCI induction). Based on our data, the correlation coefficient was 0.74±0.15 and the normalized root means square error of the brain-spine interface was 0.22±0.10. Overall, we found that using NARX, appropriate information from ECoG recordings can be extracted and used for the generation of proper intra-spinal electric stimulations for restoration of natural leg movements lost due to SCI.

Solar PV Power Estimation and Upscaling Forecast Using Different Artificial Neural Networks Types: Assessment, Validation, and Comparison

According to its various features, the solar photovoltaics (PV) system is realized as a significant promising energy source to cope with energy shortcomings and environmental impacts like contamination. Therefore, it is mandatory to estimate and predict the output power for prediction intervals to avoid any power outage or urgent disturbances in the utility grid. These are challenging tasks as the solar PV output power depends on the weather variables data such as temperature and solar radiation. In this article, the estimation and forecast of solar PV output power are investigated with an upscaling method using three different types of artificial neural networks (ANNs) in order to reduce the estimation errors in current types of ANNs. The multilayer feedforward neural network (MLFFNN), recurrent neural network (RNN), and nonlinear autoregressive exogenous (NARX) model neural network (NARXNN) are applied to estimate and forecast the total output power of four real solar PV substations in Egypt. Hence both the surface temperature and the solar radiation of each PV substation are applied as the inputs of each designed NN, whereas the total output power of the four PV substations is its output. For the training and effectiveness investigation procedures of each applied ANNs, the data of two months (60 days) are attained and collected from these four PV substations. Here, the data of the first 45 days are applied to train the three designed NNs, while the data from the remaining 15 days, which are not applied for the training, are used to check the effectiveness and the generalization capability of the trained NNs. Hence, the estimation process is considered a prior step for the forecast of the output power. Therefore, upscaling method is utilized for assessing and forecasting a regional solar PV output power because of the limited number of monitored plants and applied data. The results provide evidence that the trained NNs are running very well and efficiently to estimate the power correctly. The performance of the MLFFNN is the best compared with the other NNs, whereas the NARXNN’s performance is the lowest one. The MLFFNN achieves the lowest mean squared error (MSE) of 0.27533 and the lowest absolute approximation error of 0.2099 MWh. Finally, the assessment and comparison among the three trained NNs and other techniques in recently published articles are highlighted and presented which reveal the performance superiority of the three trained NNs compared to other ANNs.

Analyzing the impact of healthcare facilities on the spread of COVID-19: A spatial autoregressive exogenous model approach

Analyzing the impact of healthcare facilities on the spread of COVID-19: A spatial autoregressive exogenous model approach

Optimal battery state of charge parameter estimation and forecasting using non-linear autoregressive exogenous

The lithium-ion battery (LiB) has become the most widely used energy storage system for electric vehicles (EVs) due to its many advantages. The EV battery pack needs a battery management system (BMS) to estimate the state of charge (SOC) and balance the energy capacity through the cells. Apart from the fact that it is still challenging to accurately solve, the SOC forecasting represents an important concern in the study sector. This research proposes an effective battery SOC forecasting approach utilizing the non-linear autoregressive exogenous model (NARX) time’s series optimized Levenberg-Marquardt training algorithm, and Bayesian-Regularization (BR). The suggested technique is well-known for its resilience and high performance in nonlinear and complex system prediction, and it is extensively used in a wide range of disciplines. Also, the precision of the NARX technique has been investigated as a function of training data sets, error classifications based on experimental data of LiB. Both algorithms were evaluated with experimental data. Discharging followed by resting process was conducted on a 2.6 Ah LiB. They demonstrate good convergence in the low error and regression. In an effort to address a gap in the field, this paper offers a comparison between NARX-LM and NARX-BR algorithms for the LiB SOC prediction. Both algorithms are optimized the ANN using times series analysis based in the same training data. The results show that NARX-BR is more rapid and accurate with a low mean square error (MSE) of 2.39 10-5 than NARX-LM, which achieved an MSE of 1.11. Thus, it shows NARX-BR as an effective technique for LiB SOC prediction.

Auto Regressive eXogenous (ARX) System Identification of Batch Milk Cooling Process

The dynamic model of the milk cooling process from 36°C to 4°C using chilled water available at 2°C has been carried out. The cooling water temperature is kept constant by using a refrigeration unit. The process being studied was a Packo brand milk cooling tank belonging to KUD SAE Pujon (Malang - Indonesia). A fundamental heat balance method was used to derive the model, leading to a first-order transfer function process. For a 2 hours cooling process then, the gain and time constant values are 1.00 and 42.3548 mins respectively, or G(s)=1/(42.3548s+1) (first order process). Deriving system transfer function through a mechanistic model is considered difficult; therefore, in this paper, we explored process identification via Auto Regressive eXogenous (ARX). Transient simulations could then be performed to identify the dynamic behavior of the cooling process. The system was then identified using several orders of the Auto Regressive eXogenous (ARX) model, and then the results were re-tested on different forms of perturbations and obtained quite accurate results. The transfer function identified through the ARX111 is G(s)=1/(42.3729s+1) (first order process), while via ARX441, the 5th order process was obtained: G(s)=(0.02361s^4+0.000371s^3+0.2331s^2+9.27×10^(-7) s+0.0005826)/(s^5+0.03932s^4+9.873s^3+0.2331s^2+0.02468s+0.0005826). These models particularly useful for process control design and analysis.

A Feedforward Model Predictive Controller for Optimal Hydrocracker Operation

Hydrocracking is an energy-intensive process, and its control system aims at stable product specifications. When the main product is diesel, the quality measure is usually 95% of the true boiling point. Constant diesel quality is hard to achieve when the feed characteristics vary and feedback control has a long response time. This work suggests a feedforward model predictive control structure for an industrial hydrocracker. A state-space model, an autoregressive exogenous model, a support vector machine regression model, and a deep neural network model are tested in this structure. The resulting reactor temperature decisions and final diesel product quality values are compared against each other and against the actual measurements. The results show the importance of the feed character measurements. Significant improvements are shown in terms of product quality as well as energy savings through decreasing the heat duty of the preheating furnace.

A probabilistic computational framework for the prediction of corrosion-induced cracking in large structures

Corrosion can initiate cracking that leads to structural integrity reduction. Quantitative corrosion assessment is challenging, and the modeling of corrosion-induced crack initiation is essential for model-based corrosion reliability analysis of various structures. This paper proposes a probabilistic computational analysis framework for corrosion-to-crack transitions by integrating a phase-field model with machine learning and uncertainty quantification. An electro-chemo-mechanical phase-field model is modified to predict pitting corrosion evolution, in which stress is properly coupled into the electrode chemical potential. A crack initiation criterion based on morphology is proposed to quantify the pit-to-cracking transition. A spatiotemporal surrogate modeling method is developed to facilitate this, consisting of a Convolution Neural Network (CNN) to map corrosion morphology to latent spaces, and a Gaussian Process regression model with a nonlinear autoregressive exogenous model (NARX) architecture for prediction of corrosion dynamics in the latent space over time. It enables the real-time prediction of corrosion morphology and crack initiation behaviors (whether, when, and where the corrosion damage triggers the crack initiation), and thus makes it possible for probabilistic analysis, with uncertainty quantified. Examples at various stress and corrosion conditions are presented to demonstrate the proposed computational framework.