Modeling Estimation Research Articles

Modeling and Fault Size Estimation for Non-Penetrating Damage in the Outer Raceway of Tapered Roller Bearing

The fault quantification of a tapered roller bearing (TRB) can provide a reliable guide for predictive maintenance. Currently, damage size estimation based on vibration signals has been proposed and developed. However, most approaches are focused on the theory of ball bearings. Unlike the point contact of a ball bearing, the contact between the tapered roller and the raceway is a line contact. So, the current practices based on the micro-motion theory of ball bearings are limited when estimating the TRB’s fault. To accurately estimate the TRB’s damage size, a dynamic model of non-penetrating damaged TRB was established to research the vibration response mechanism and explain the influence. The model takes the deflection factor of a tapered roller into consideration and uses the elastohydrodynamic lubrication model to simulate the influence of the lubrication factor. Then, a revised formula for estimating the TRBs’ fault size is proposed by uncovering the relationship between the impact time and the damage location. Simulation analysis and experimental analysis prove the correctness of the dynamic model and the effectiveness of the size estimation formula.

Modeling and analysis of nonlinear axial friction forces of tripod joints considering roughness characteristics of contact pairs

The nonlinear axial friction force (NAFF) induced by contact and friction between components of tripod joints constitutes an important dynamic characteristic of drive-shaft systems, which will lead to deterioration of the NVH (noise, vibration and harshness) performance of the vehicle. Based on the fractal theory, this paper proposes a novel modeling and analysis method for accurate estimation of NAFF considering micro contact and friction characteristics of contact pairs. An analytical model is developed for estimating the NAFF of tripod joints considering micro contact and friction characteristics of contact pairs. In the process of modeling, a modified distribution function is proposed to describe distributions of contact asperities between the curved surfaces to describe the contact and friction states between the rollers and the tracks inside tripod joints more accurately. The developed analytical model suggests that NAFF is related to the fractal parameters, the material parameters, the operating conditions and the distribution of asperities. The effectiveness of the analytical model is demonstrated by comparing the model-predicted NAFF with the measured data. Based on the developed model of NAFF, the influences of important input parameters on the magnitude of NAFF are qualitatively analyzed. Subsequently, Sobol' global sensitivity analysis method is used to further investigate the influences of important input parameters on the magnitude of NAFF. The results show that the proposed modeling and analysis method for estimating NAFF are essential to characterize relationships among NAFF and fractal parameters, material properties and distribution of asperities from a micro level, which could facilitate design optimization of the tripod joints.

Control-Based Continuation: A New Approach to Prototype Synthetic Gene Networks.

Control-Based Continuation (CBC) is a general and systematic method to carry out the bifurcation analysis of physical experiments. CBC does not rely on a mathematical model and thus overcomes the uncertainty introduced when identifying bifurcation curves indirectly through modeling and parameter estimation. We demonstrate, in silico, CBC applicability to biochemical processes by tracking the equilibrium curve of a toggle switch, which includes additive process noise and exhibits bistability. We compare the results obtained when CBC uses a model-free and model-based control strategy and show that both can track stable and unstable solutions, revealing bistability. We then demonstrate CBC in conditions more representative of an in vivo experiment using an agent-based simulator describing cell growth and division, cell-to-cell variability, spatial distribution, and diffusion of chemicals. We further show how the identified curves can be used for parameter estimation and discuss how CBC can significantly accelerate the prototyping of synthetic gene regulatory networks.

Combined empirical and machine learning modeling method for estimation of daily global solar radiation for general meteorological observation stations

Daily global solar radiation (H) is practically significant for human production and life, especially for solar power generation. Due to the high construction and maintenance cost of solar radiation observation equipment, solar radiation measurement cannot be easily obtained at many sites. In addition, the H estimation fitting model cannot be directly trained in the absence of site-specific historical data of H. Therefore, in this study, the H estimation modeling method is proposed specifically for the sites that cannot afford to install solar radiation measurement equipment. The method includes a novel coding method based on information gain, Pearson correlation coefficient, and principal component analysis (PCA), which analyzes the nonlinear and high-dimensional correlation between meteorological factors and solar radiation to decide the correlation of adjacent sites. Based on the results of the coding method, a hybrid H estimation model combining empirical and machine learning is proposed, which takes the empirical model as the base model, and the machine learning model adaptively assigns the corresponding weights to estimate H. The case studies show the proposed hybrid model outperforms the benchmark models and indicate that the H estimation modeling method can be applied to different regions without solar radiation measurement.

Inverse unsaturated-zone flow modeling for groundwater recharge estimation: a regional spatial nonstationary approach

Inverse unsaturated-zone flow modeling for groundwater recharge estimation: a regional spatial nonstationary approach

Analysis Model of Consumer Sentiment Tendency of Commodities in E-Commerce.

Users are increasingly turning to the internet to acquire and consume goods. Online purchasing builds demand between customers in modern years. E-commerce (e-commerce) is a business strategy that allows individuals and businesses to buy and sell goods and services through the Internet. Ecommerce can be used on computers, tablets, cellphones, and other smart devices, and it operates in four key market categories. The way individuals buy and consume goods and services has changed as a result of e-commerce. People are increasingly using their computers and smart devices to place orders for things that can be delivered quickly to their homes. In the 1960s, ecommerce made use of an electronic system called electronic data interchange to help in document conversion. In the world of e-commerce, Amazon is a monster. It is, in reality, the world's largest online store, and it is still growing. As a result, it has become a significant roadblock in the retail industry, prompting some major merchants to rethink their plans and adjust their focus. This article is based on literary reviews. Developing a research framework for consumer trends, particularly in terms of purchasing behavior, is very much necessary. The sample size for this investigation was determined using a simple rule of thumb for successful partial least squares structural equation modeling (PLS-SEM) estimation. Consumer sentiment tendencies play a major role in this research. This research's most valuable factors include a promotion, price, brand loyalty, product review, and product quality. We looked into how these aspects analyzed a customer's tendency. These are the primary topics of discussion in this study.

Data-driven modeling of the temporal evolution of breakers’ states in the French electrical transmission grid

In electrical transmission grids, it is common to observe the states of circuit breakers. While they are known at irregular times, system modeling and grid state estimation are of the highest importance to ensure secure operations. This paper proposes a richer method to estimate the grid state over its reference configurations based on the temporal evolution of its breakers’ states. The first contribution consists in developing a general multi-observation continuous-time finite-state Hidden Markov Model with filter-based parameter estimation to infer the hidden state (e.g., the grid reference configuration) handling multiple observed processes with irregular “jump” times (e.g., the breakers’ states). As a second contribution, we build a numerical scheme with no discretization error adapted to all state jumps generated by the observed processes. Finally, we apply our model to simulated and real data to illustrate the approach’s performance. The available data consists of historical records of breakers’ states during the electrical transmission grid operated normally. For this real-data-driven application, we also present a clustering approach to identify the set of grid reference configurations.

A machine learning approach to small area estimation: predicting the health, housing and well-being of the population of Netherlands

BackgroundLocal policymakers require information about public health, housing and well-being at small geographical areas. A municipality can for example use this information to organize targeted activities with the aim of improving the well-being of their residents. Surveys are often used to gather data, but many neighborhoods can have only few or even zero respondents. In that case, estimating the status of the local population directly from survey responses is prone to be unreliable.MethodsSmall Area Estimation (SAE) is a technique to provide estimates at small geographical levels with only few or even zero respondents. In classical individual-level SAE, a complex statistical regression model is fitted to the survey responses by using auxiliary administrative data for the population as predictors, the missing responses are then predicted and aggregated to the desired geographical level. In this paper we compare gradient boosted trees (XGBoost), a well-known machine learning technique, to a structured additive regression model (STAR) designed for the specific problem of estimating public health and well-being in the whole population of the Netherlands.ResultsWe compare the accuracy and performance of these models using out-of-sample predictions with five-fold Cross Validation (5CV). We do this for three data sets of different sample sizes and outcome types. Compared to the STAR model, gradient boosted trees are able to improve both the accuracy of the predictions and the total time taken to get these predictions. Even though the models appear quite similar in overall accuracy, the small area predictions at neighborhood level sometimes differ significantly. It may therefore make sense to pursue slightly more accurate models for better predictions into small areas. However, one of the biggest benefits is that XGBoost does not require prior knowledge or model specification. Data preparation and modelling is much easier, since the method automatically handles missing data, non-linear responses, interactions and accounts for spatial correlation structures.ConclusionsIn this paper we provide new nationwide estimates of health, housing and well-being indicators at neighborhood level in the Netherlands, see ’Online materials’. We demonstrate that machine learning provides a good alternative to complex statistical regression modelling for small area estimation in terms of accuracy, robustness, speed and data preparation. These results can be used to make appropriate policy decisions at a local level and make recommendations about which estimation methods are beneficial in terms of accuracy, time and budget constraints.

Conditional functional clustering for longitudinal data with heterogeneous nonlinear patterns

In studies of cognitive aging, it is crucial to distinguish subtypes of longitudinal cognition change while accounting for the effects of given covariates. The longitudinal cognition trajectories and the covariate effects can both be nonlinear with heterogeneous shapes that do not follow a simple parametric form, where flexible functional methods are preferred. However, most functional clustering methods for longitudinal data do not allow controlling for the possible functional effects of covariates. Although traditional mixture-of-experts methods can include covariates and be extended to the functional setting, using nonlinear basis functions, satisfactory parsimonious functional methods required for robust functional coefficient estimation and clustering are still lacking. In this paper we propose a novel latent class functional mixed-effects model in which we assume the covariates have fixed functional effects, and the random curves follow a mixture of Gaussian processes that facilitates a model-based conditional clustering. A transformed penalized B-spline approach is employed for parsimonious modeling and robust model estimation. We propose a new iterative-REML method to choose the penalty parameters in heterogeneous data. The new method is applied to the latest data from the Religious Orders Study and Rush Memory and Aging Project, and four novel subtypes of cognitive changes are identified.

A Mathematical Framework for Learning Probability Distributions

The modeling of probability distributions, specifically generative modeling and density estimation, has become an immensely popular subject in recent years by virtue of its outstanding performance on sophisticated data such as images and texts. Nevertheless, a theoretical understanding of its success is still incomplete. One mystery is the paradox between memorization and generalization: In theory, the model is trained to be exactly the same as the empirical distribution of the finite samples, whereas in practice, the trained model can generate new samples or estimate the likelihood of unseen samples. Likewise, the overwhelming diversity of distribution learning models calls for a unified perspective on this subject. This paper provides a mathematical framework such that all the well-known models can be derived based on simple principles. To demonstrate its efficacy, we present a survey of our results on the approximation error, training error and generalization error of these models, which can all be established based on this framework. In particular, the aforementioned paradox is resolved by proving that these models enjoy implicit regularization during training, so that the generalization error at early-stopping avoids the curse of dimensionality. Furthermore, we provide some new results on landscape analysis and the mode collapse phenomenon.

An Information Appraisal Procedure: Endows Reliable Online Parameter Identification to Lithium-Ion Battery Model

Online parameter identification is vital for boosting the accuracy of the battery equivalent circuit model (ECM) under dynamic profiles. However, traditional recursive least squares (RLS) method easily decays with the noise corruption from sensors or insufficient exciting signal in reality, which further limits the performance of ECM in battery modeling and states estimation. This article thus proposes a reliable online parameter identification method for battery ECM, which utilizes a well-designed information appraisal procedure based on the Fisher-information-based Cramer–Rao lower bound (CRLB). Without increasing much computing complexity, a comprehensive appraisal indicator, derived recursively from CRLB, enables a new mechanism for online parameter updating. Simulation and experimental results prove the validity of the proposed method under different driving cycles, temperatures, and aging conditions. The results show that the identification accuracy of the proposed method has been significantly improved comparing with a typical RLS and a multiple adaptive forgetting factors RLS method.

Modeling and Parameter Estimation of a Steam Power Plant Including Condenser Back-Pressure Uncertainty Using Operational Data

This article copes with a challenging issue about modeling and parameter estimation of the steam power plants. To this end, a thorough model for steam units is obtained, where the turbine-boiler control strategy is also considered in the modeling procedure. The number of integrated model inputs is optimal, making it appropriate for automatic generation control purposes. This model can work accurately in nominal conditions without abnormal events. Thus, the gathered data from an identification test in a 325 MW steam unit in nominal situations is employed to estimate the proposed model's unkn0own parameters using a genetic algorithm. Additionally, the condenser back-pressure (CBP) effect in the steam unit generation is elaborated. Regarding the CBP dependency on environmental conditions, like the wind speed, this effect is treated as an uncertainty in the article. Consequently, a novel turbine model considering the CBP effect is introduced. Determining the CBP uncertainty bounds is another problem addressed in the article using an efficacious information gap decision theory method on the recorded data in abnormal conditions. Finally, the derived model and parameters in both nominal and abnormal conditions are validated using the operational data, and the results are discussed.

Simplified estimation modeling of land surface solar irradiation: A comparative study in Australia and China

Solar irradiation maps are fundamental geospatial datasets that have been used in a variety of research fields. However, it is difficult to estimate the continuous distribution of solar irradiation over large areas accurately by using conventional interpolation or extrapolation methods based on only a few observation stations. To tackle this problem, this study proposed a method to estimate spatially continuous land surface solar irradiation based on four machine learning models, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron (MLP). Clear-sky solar irradiation data were computed based on time and location, cloud optical thickness (COT) and aerosol optical thickness (AOT) that significantly influence solar irradiation were retrieved from Himawari-8 meteorological satellite images, and land surface solar irradiation data were obtained from observation stations for training and evaluation. To explore the weather effects on land surface solar irradiation, air temperatures, humidity, wind, and atmospheric pressure were also quantified and integrated into the models. As a comparative study, this study collected six-year historical data and estimated solar distribution at a 5-km spatial resolution in Australia and China. Based on the coefficient of determination (R2), normalized Root Mean Square Error (nRMSE), normalized mean bias error (nMBE), and consumption of time (t), the results show that GBM achieved the highest accuracy with R2>0.7 at all stations, followed by RF, SVR, and MLP. It suggests that the proposed method can provide an accurate and reliable estimation of land surface solar irradiation, compared with the theoretical solar irradiation without the obstacle of the atmosphere. The annual solar distribution maps created by the built methods indicate that the proposed method is simple and effective for large geographical regions and can be used worldwide when similar datasets are obtained.

Synthesis, evaluation, and optimal stability of a biowaste-based catalytic oxidative desulfurization of model fuel in a trickle bed reactor

Oxidative Desulfurization (ODS) is one of the promising alternative processes. However, the lifetime of the ODS catalysts is an issue that restricted the use of the process. In this work, Activated Carbon (AC) prepared from biowaste (apricot stones (AS)) was impregnated with Mn and coated with aluminum oxide to prolong the lifetime of the ODS process. To evaluate the performance of the prepared catalysts for a continuous three-phase ODS process, a trickle bed reactor (TBR) was used to conduct the ODS process at mild oxidation temperatures. The ODS evaluation study was conducted under different temperatures (60 and 70 °C), LHSVs (1, 1.35, 2, and 4 h-1), pressures (6 and 800 kPa), and initial sulfur concentrations (100 and 300 ppm). Both coated and uncoated catalysts showed high removal efficiency of the n-butyl mercaptan in the TBR. The maximum conversions obtained were 97.29 % and 95.19 % for the coated and uncoated Mn/AC catalysts at T = 70 °C, P = 800 kPa, LHSV= 1 hr-1, and initial sulfur concentration= 300 ppm. Besides the high performance, the coated Mn/AC showed outstanding stability during the continuous oxidation process.A kinetic model was formulated for the ODS process for the estimation of kinetic parameters of the high stability process.gPROMS has been used for modeling, simulation, and parameter estimation via optimization. The simulation results showed good consistency with the experimental data of all lumps with an absolute average error of up to 5 % and the optimization resulted in the optimal kinetics parameters of the high stability ODS process

A methodology to assess the causation relationship of seafarers’ unsafe acts for ship grounding accidents based on Bayesian SEM

Unsafe acts have been recognized as the substantial factors leading to maritime accidents. An obstacle to analyzing seafarers' unsafe acts involved in maritime accidents is the lack of data. Bayesian approach in structural equation modelling (SEM) estimation has attracted extensive attentions across other fields, due to its advantages that can handle complicated models and small sample size. Therefore, this study proposes an innovative methodology to address the multifactor risk coupling and causation involved in seafarers' unsafe act contributing to ship grounding accidents. Based on the principle of Bayesian SEM, the advantages of Grounded theory and Human Factors Analysis and Classification System (HFACS) are integrated into this methodology. Specifically, Grounded theory and HFACS model are applied to identify and structure seafarers' unsafe acts and their contributory factors, facilitating the development of the conceptual model. Then, by employing Bayesian SEM, the causation paths and effects of seafarers' unsafe acts are examined. The results reveal that organizational influences play a significant role in the causation mechanism of seafarers' unsafe acts, and preconditions for unsafe acts are the only variables that exert direct impact on unsafe acts. Moreover, unsafe supervision and preconditions for unsafe acts are served as intermediate factors affecting seafarers' unsafe acts. In addition, this results indicate the feasibility of Bayesian SEM in analyzing seafarers’ unsafe acts.

Modeling of software project effort estimation: a comparative performance evaluation of optimized soft computing-based methods

Modeling of software project effort estimation: a comparative performance evaluation of optimized soft computing-based methods

Comparative study of artificial neural network versus parametric method in COVID-19 data analysis

Since the previous two years, a new coronavirus (COVID-19) has found a major global problem. The speedy pathogen over the globe was followed by a shockingly large number of afflicted people and a gradual increase in the number of deaths. If the survival analysis of active individuals can be predicted, it will help to contain the epidemic significantly in any area. In medical diagnosis, prognosis and survival analysis, neural networks have been found to be as successful as general nonlinear models. In this study, a real application has been developed for estimating the COVID-19 mortality rates in Italy by using two different methods, artificial neural network modeling and maximum likelihood estimation. The predictions obtained from the multilayer artificial neural network model developed with 9 neurons in the hidden layer were compared with the numerical results. The maximum deviation calculated for the artificial neural network model was −0.14% and the R value was 0.99836. The study findings confirmed that the two different statistical models that were developed had high reliability.

Modeling of access resistances and channel temperature estimation for GaN HEMT

Modeling of access resistances and channel temperature estimation for GaN HEMT

Solar Geoengineering Modeling and Applications for Mitigating Global Warming: Assessing Key Parameters and the Urban Heat Island Influence

In this paper, solar geoengineering modeling is presented with a goal to simplify reverse forcing assessments and the capability to apply it to a wide variety of applications. Results find improvements on sun-shade space mirror and desert surface treatment estimates, stratosphere sun-dimming methods, and the Urban Heat Islands (UHIs) influence. A heat amplification parameter is added to the model allowing it to be applied to UHI estimates. UHI amplification effects are due to the large solar area of buildings, reduction of wind cooling, solar canyons, and so forth. The UHI reverse forcing requirements are assessed with amplification estimates of 3.1 and 5.2, yielding 7.6% to 12.7% of gross global warming could be due to the urbanization effect, respectively. The gross warming 7.6% estimate, accurately compares to the author's prior study, and the 12.7% represents very recent results by other authors from new measurement methods. Key issues are pointed out that without including a heat amplification estimate and other modeling parameters, the UHI intensity, that likely dominates the urbanization warming effect could be severely underestimated, yielding urbanization estimates possibly as low as 2.4%. It is important to identify possible reasons where underestimates may occur from a modeling perspective to help understand controversies that may be occurring. The new model helps to clarify such parameters, allows for a significant reduction in complexity and calibration, and is shown to be helpful for numerous solar geoengineering applications including the serious need to reduce the UHI effect worldwide. Solar geoengineering solutions will require a lot of creativity, in addition to modeling, suggestions are provided for drought relief ideas and Paris Accord goals required for any successful urban solar geoengineering coordinated effort.

A Joint State Estimation Framework for Lithium-ion Batteries based on Hybrid Method

Accurate estimation of state of charge (SOC), state of health (SOH) and remaining useful life (RUL) of Lithium-ion batteries is of great importance. This paper proposes a estimation framework based on charging voltage segment and hybrid method combining with equivalent circuit model and data-driven algorithms to achieve the accurate estimation of SOC, SOH and RUL of the long life cycle of the battery. The charging voltage segment, with the merit of flexibility and available, which contains much information for battery degradation, is suitable for battery modeling and state estimation. The rising time of the optimal charging voltage segment which has the highest correlation with the present capacity is extracted as health feature (HF) to estimate the SOH with least squares support vector machine (LSSVM), and the identified resistance and capacitance parameters acquired by fitting the charging voltage segment with equivalent circuit model and estimated capacity acquired by LSSCM are used to estimate SOC with the unscented Kalman filter (UKF) algorithm in the discharge stage for this cycle; Gaussian process regression (GPR) is used to prediction the trend of HF, which are combined with established LSSVM model to estimate RUL. Experimental results show that the proposed method can estimate SOC, SOH and RUL jointly with high accuracy.