Grey Model Method Research Articles

Evaluation of critical success determinants to the implementation of additive manufacturing technology in the spare parts supply chain: a grey causal modelling approach

PurposeAdditive Manufacturing technology (AMT) is swiftly gaining prominence to induce automation and innovation in manufacturing systems. It holds immense potential to change supply chain dynamics by providing the possibility of printing objects on demand. This study thus formulates and analyzes the framework to incorporate AMT to handle the spare parts supply chain management (SPSCM) in capital-intensive industries by identifying and assessing the critical success factors (CSFs).Design/methodology/approachAssessment of the CSFs is performed using the novel Grey Causal Modeling method (GCM) with the objective of making SPSCM resilient and efficient. GCM conducts causal analysis by taking into consideration cause, effects, the objectives, and the situations.FindingsFindings indicate that; Logistics Lead Time (SD4), Time to manufacture (SD3), Management Support (SD11), and Risk Management (SD20) are the most prominent causal factor having a maximum impact when incorporating AMT in SPSCM. The results also reveal that the performance of manufacturing organizations that adopt AMT is substantially influenced by internal and external factors such as Management Support (SD11) and Government Regulations (SD16).Research limitations/implicationsThis research provides valuable information for getting the global spare parts supply chain equipped for the post-COVID age, where digital technologies such as AMT will be fundamental for bolstering supply chain resilience and efficiency.Originality/valueThis research proposes a framework for performance assessment when incorporating AMT in SPSCM. Study also demonstrates methodological application of novel Grey Causal Modelling technique using a real case in a spare parts manufacturing industry in India.

Analysis of surface deformation and related factors over mining areas based on InSAR: A case study of Fengcheng mine

Abstract. Ground surface deformation in mines affects mining production, damages the ecological environment, and endangers human safety. Mastering the detailed time series deformation and related triggering factors can provide key information for the safety of the mining area. Therefore, the Fengcheng mining area, a large and ancient coal mine in Jiangxi Province, China, was selected as the study area in this work, and the following research was conducted: 1. The accuracy and applicability of the Stacking, Small-Baseline Subset InSAR (SBAS-InSAR), and Interferometric Point Target Analysis (IPTA) methods were preliminarily explored while monitoring the annual deformation rate based on Sentinel-1A data from October 2019 to November 2022. 2. The time-series deformation of the Fengcheng mining area was obtained with SBAS-InSAR technology, and the sedimentation was validated with leveling results. 3. The correlation factors of deformation, such as rainfall and land cover, were studied, and the relationship between the influencing factors, such as coal mining dip angle, digital elevation, coal mining elevation, and deformation, was quantitatively explored with the Grey correlation model and Pearson correlation analysis method. The following conclusions were drawn: The SBAS method has the best adaptability in the dense vegetation mining area, and the root-mean-square error of the difference between deformation results and leveling data does not exceed 4mm. The evolution process of deformation centers is mainly divided into the stages of initial deformation, constant velocity deformation, accelerated deformation, and stable condition. Compared with the natural factors, the settlement of the Fengcheng mining area is mainly affected by human-induced mining and construction of artificial facilities.

Grey Modeling Method for Predicting the Occurrence and Epidemic Time of Major Human Diseases

Grey Modeling Method for Predicting the Occurrence and Epidemic Time of Major Human Diseases

A Generalization of the Grey Lotka–Volterra Model and Application to GDP, Export, Import and Investment for the European Union

This study proposes a generalized grey Lotka–Volterra model with a finite number of variables. The model is obtained by applying the grey modelling method to estimate the parameters of a finite dimensional quadratic Lotka–Volterra system. Subsequently, the model is used to analyze the competition and cooperation relationship between four macroeconomic indicators, namely Gross Domestic Product, Export, Import and Investment, and to obtain short-time forecasting for them. The data used in the empirical investigation cover the time periods 2005–2022 and 2011–2022, for the European Union. The empirical results are compared to the ones obtained by using the grey model GM(1,1) and the two-dimensional grey Lotka–Volterra model. Finally, economic interpretations of the empirical findings are formulated.

Grey Box Modeling of Gas Temperature for a High-Speed and High-Temperature Heat-Airflow Test System

Aiming at a problem that it is difficult for accurately obtaining the mathematical model of a gas temperature for a high-speed, high-temperature heat-airflow test system, a grey box modeling method combining first principle of modeling, recursive least squares identification with auxiliary variable and correlation analysis is proposed, and the precise mathematical model of gas temperature is established. Firstly, the preliminary mathematical model of gas temperature is obtained by using the first principle modeling method, and the dynamic behaviors of the system are analyzed; the prior knowledge of the system is obtained, and the parameters that need to be identified are pointed out. Secondly, the basic principles of recursive least square identification with auxiliary variables and correlation analysis are introduced, and the uncertain parameters of the gas temperature model are identified by using the introduced methods. Finally, the precise mathematical model of gas temperature is obtained by simulation and experimental research. The research results show that the mathematical model of gas temperature established by the grey box modeling method put forward in this article has satisfactory accuracy.

Study on Rolling Bearing Life Based on Weibull Distribution and Correlation Coefficient Optimization and Maximum Likelihood Estimation

For the reliability evaluation of rolling bearing life, this paper proposes a parameter estimation method based on the correlation coefficient optimization method and the maximum likelihood estimation method (Cor-MLE), which can obtain more accurate Weibull distribution parameter estimation. Subsequently, the feasibility of Cor-MLE method for Weibull parameter estimation was verified by Monte Carlo simulation data and rolling bearing life test data. Compared with the parameter estimation results of least square method, grey model method and correlation coefficient optimization method, it is proved that the estimation results of this method have high accuracy and can meet the life analysis of rolling bearings.

Dynamic Evaluation and Regional Differences Analysis of the NEV Industry Development in China

In the transportation sector, new energy vehicles (NEVs) are critical to reduce CO2 emissions in the context of carbon neutralization. The study of dynamic evaluation and regional difference analysis is helpful to the NEV industry development in policy design and industrial planning. In this study, based on the provincial data in China from 2016 to 2020, the grey target model and Dagum Gini coefficient method are employed for the dynamic evaluation and regional differences of the NEV industry development. The results were as follows: (1) The overall and provincial level of the NEV industry development showed an increasing pattern. The bull’s eye degrees of Guangdong, which had the best development, were 0.4884, 0.5361, 0.6067, 0.6787, and 0.7047 during the study period. (2) The regional differences in the NEV industry development were significant. The east region had the best development, followed by the middle, the west, and the northeast regions. The intra-regional differences were expanding with different annual growth rates. (3) The inter-regional differences between the east and the other three regions were the largest. The regional differences in the NEV development are mainly derived from inter-regional dereference. (4) The D1, D2, and D3 dimensions all contributed significantly to provinces with higher levels of development, while the D4 dimension contributed significantly to provinces with lower levels of development. Based on these results, different provinces should take differentiated development strategies and enhancement paths to promote their NEV industry development.

A novel seasonal grey model for forecasting the quarterly natural gas production in China

As a green and clean source of energy, natural gas plays an indispensable role in changing the energy structure and reducing greenhouse gas emissions. In this context, forecasting natural gas production becomes particularly important. In China, natural gas production has obvious seasonal and cyclical characteristics. To this end, a grey prediction model combined with particle swarm optimization (PSO) is proposed: the PSO-based data grouping grey model with a fractional order accumulation (PSO-FDGGM (1,1)). The traditional grey model has shortcomings such as low prediction accuracy and poor adaptability, and its application is limited. Compared with the traditional grey models, this model can be used to predict time series with seasonal characteristics, and the prediction accuracy is higher. Based on the data grouping-based grey modelling method (DGGM (1,1)), this model introduces the modeling method of the grey system model with the fractional order accumulation (FGM (1,1)), that is, combining data grouping with fractional accumulation, and using PSO to optimize fractional-order parameters, which improves the performance and adaptability of the model. In order to verify the effectiveness of the model, the model is compared with the traditional grey model (GM (1,1)), FGM (1,1) model, DGGM (1,1) model, autoregressive comprehensive moving average (ARIMA) model and the PSO-based grey forecasting model (PSOGM (1,1)). The mean absolute percent errors (MAPEs) of the comparison model in the test set are 7.46%, 9.28%, 4.38%, 8.60% and 5.97% respectively, while that of PSO-FDGGM (1,1) model is only 3.19%. The results show that PSO-FDGGM (1,1) has good adaptability and prediction accuracy for seasonally fluctuating sequences. In order to further verify the effectiveness and applicability of PSO-FDGGM (1,1), this paper adopts two ways: adjusting the parameters of PSO algorithm in the new model and applying the new model to the prediction of solar power generation, and the empirical results show that the new model is effective in forecasting seasonal data. Finally, the new model is used to predict the quarterly production of natural gas in China from the third quarter of 2021 to the second quarter of 2024. The forecast results can provide a basis for formulating natural gas production plans and environmental policies.

Imputing missing data using grey system theory and the biplot method to forecast groundwater levels and yields

Groundwater management is one of today’s important tasks. It has become necessary to seek out increasingly reliable methods to conserve groundwater resources. Dependable forecasting of the amounts of groundwater that can be abstracted in a sustainable manner requires longterm monitoring of the groundwater regime (rate of abstraction and groundwater levels). Monitoring of the groundwater source for the town of Bečej, Serbia had been disrupted for multiple years. The objective of the paper is to assess the possibility of reinterpreting the missing data or, in other words, to reconstruct the operation of the groundwater source and its effect on groundwater levels. At the Bečej source, groundwater is withdrawn from three water-bearing strata comprised of fine- to coarse-grained sands. Historic data are used to reconstruct the operation of the Bečej source between 1st of October 1980 to 1st of May 2010. The monitored parameters are total source yield and piezometric head at seven observation wells and 14 pumping wells. A data reconstruction methodology was developed, which included the use of an autoregressive (AR) model, a grey model (GM), and the biplot method. The methodology is applied to fill the data gaps during the considered period. The paper also describes the criteria for evaluating the accuracy of the AR model, GM, and biplot method. The proposed data reconstruction approach yielded satisfactory results and the methodology is deemed useful for the Bečej source data, as well as other historic data not necessarily associated with groundwater sources, but also groundwater control and protection systems, as well as hydrometeorological, hydrological and similar uses.

New Product Short-Term Demands Forecasting with Boxplot-Based Fractional Grey Prediction Model

The cost of investing in new product development (NPD) is high, and it is a feasible way to use demand forecasts for customer or end-users as a decisive reference. However, this short-term time-series data has difficulties in learning because there is no past performance on which to base the estimates. In the past, it has been proven that the cumulative method of the fractional grey prediction model (FGM) is better than the traditional integer cumulative method of the grey model (GM) model. There are many studies using different optimal algorithms to determine the moderate score order. How to set the coefficient of α in FGM is also worth exploring. Therefore, this research reveals a new fractional grey prediction model which uses box-and-whisker plots to estimate the trends of data, known as the boxplot-based fractional scale prediction model (boxplot-based FGM, BP-FGM) to improve the accuracy of predictors by setting the coefficient sets of α. In the experiment, the examined dataset was collected from a well-known equipment manufacturer as the research object. For modeling, the mean absolute percentage error (MAPE) was established as the objective function of the optimization model, the results from three datasets verified the effect through the commodity attributes and public test data of its production, and the experimental results show that BP-FGM has better prediction results than FGM.

Mid- and long-term strategy based on electric vehicle charging unpredictability and ownership estimation

Predicting the charging load of electric vehicles (EVs) is critical for the safe and reliable operation of the distribution network. Analyzing an EV's random charging characteristics and the uncertainty associated with its development scale are important to accurate prediction of its charging load. For this reason, we proposed a seminal method for predicting EV charging load based on stochastic uncertainty analysis. This included not only a probabilistic load model for describing the stochastic characteristics of the EV charging, but also an ownership forecasting model for estimating the EV development scale. EVs are classified into four categories based on their intended use: electric buses, electric taxis, private EVs, and official EVs. The corresponding load calculation model was developed by analyzing the charging behavior of various EVs. Simultaneously, the improved grey model method (IGMM) based on the Fourier residual correction is used to accurately forecast EV ownership. Finally, the scientific method of Monte Carlo simulation(MCS) was used to estimate the charging load demand of EVs. This method was used in Wuhan that has a lot of potential for EV production. As compared to the basic grey model method (BGMM), the IGMM outlined in this work can triple the prediction effect. Due to the large-scale charging of EVs, Wuhan's maximum daily total load would rise to 15,532.9 MW on working days and 15,475.5 MW on rest days in 2025. Additionally, the total load curves on working days and rest days will show a new peak load with the value of 14751.3 MW and 14787.2 MW at 14:01, resulting in an increase of 13.56% and 13.83% respectively in the basic daily load stage. As a result, it is necessary for grid operators to build adequate capacity to meet EV charging demands, while developing rational and orderly charging strategies to avoid the emergence of new load peaks.

A Novel Simultaneous Grey Model SGM(1, 2) and Its Applications in Prediction

The common models used for grey system predictions include the GM(1, 1), the GM(1, <i>N</i>), the GM(<i>N</i>, 1), and so on, but their whitening equations are all single ordinary differential equations. However, objects and factors generally form a whole through mutual restrictions and connections when the objective world is developing and changing continuously. In other words, variables affect each other. The relationship can't be properly reflected by the single differential equation. Therefore, the paper proposes a novel simultaneous grey model. The paper gives a modeling method of simultaneous grey model SGM(1, 2) with 2 interactive variables. The example proves that the simultaneous grey model has high precision and improves the precision significantly compared with the conventional single grey model. The new method proposed enriches the grey modeling method system and has important significance for the in-depth study, popularization and application of grey models.

Deep Integration of Physical Health Education Based on Intelligent Communication Technology.

In recent years, intelligent medical communication technology has developed rapidly, and the advancement of hardware technology has accelerated the development of software technology. Physical exercise breaks the limits of all aspects of the human body and requires scientific and reliable information. Industrial cameras realize collection and high-resolution image processing functions and use artificial intelligence to create conditions for collecting scientific data. With the powerful driving force of artificial intelligence, it is believed that physical education will develop to a new height. The intellectualization of physical education makes people expect that it will also bring some inevitable problems, but as long as we continue to overcome and improve, we believe that artificial intelligence will better serve physical education and benefit mankind. Artificial intelligence is the current trend of social development. This paper takes physical education as a breakthrough point to discuss the application of artificial intelligence in physical education. This paper uses the grey GM prediction model and questionnaire survey method to build an online education platform and develop an effective education model, which can integrate the country's best educational resources and reduce time and space constraints. Providing a relaxing learning environment for education, one can learn anytime, anywhere. In a typical physical education system, the average self-efficacy score is converted from 20.48 points to 68.37 points, and the average self-score of physical education in the integrated artificial intelligence environment is converted from 30.25 points to 75.54 points. The data show that the quality of education has greatly improved.

Novel fuzzy direct torque control based on constructed functional transformed grey model

To reduce the ripple of the magnetic flux and torque of motors and the reduce the hysteresis in motor speed control, an improved grey model predictive fuzzy direct torque control (DTC) method based on function transformation is proposed. First, a function transformation is used to transform the sampled sequences to nonnegative values. This overcomes the disadvantages caused by fluctuant and random sampling of the motor torque and stator flux linkage. Second, an equal dimensional new information model is used to keep the dimensions unchanged, which reduces the time to predict the motor parameters through the model. Moreover, the voltage space vector plane is divided into six sectors, which simplifies the fuzzy control system rules. Simulation results show that the proposed fuzzy direct torque control based on the improved grey model method reduces the influence of hysteresis on the control system, decreases the motor flux chain and torque ripple, improves the response speed of the torque and rotational speed, reduces overshoot, achieves good effects in terms of anti-interference capability and dynamic response, and improves the real-time performance and accuracy of the fuzzy control system.

Bearing Life Prediction Based on SPSS and Grey Prediction Model

Many sophisticated weapons and equipment have bearings, which can play a vital role of reducing friction to improve the efficiency of equipment. The performance of CV can quantify the processing of the bearing, it will make the staff have a reference standard to evaluate the working condition of the bearing. Primarily, according to the known data, this paper calculated the real CV value with the maximum likelihood estimation method. Both SPSS curve estimation and the method of cumulative grey prediction model were used to train the part of the real data. Then, the failure moment of bearings failure moment can be simulated. The final step was to calculate the relative error of predicted CV value. Compared with the predicted results of the original grey prediction model (GM (1,1)), the three relative errors were 6.9502 percent, 4.6433 percent and 10.5938 percent. Three methods above all have certain practical value in engineering. However, the effect of cumulative grey prediction is better obviously.

Forecasting the electronic waste quantity with a decomposition-ensemble approach

Waste electrical and electronic equipment (viz., WEEE or e-waste) is the fastest-growing type of hazardous solid waste in the worldwide. The accurate prediction of the amount of e-waste might help improve the efficiency of e-waste disposal. In this study, a novel decomposition-ensemble-based hybrid forecasting methodology that integrates variational mode decomposition (VMD), exponential smoothing model (ESM), and grey modeling (GM) methods (named VMD-ESM-GM) is proposed for e-waste quantity prediction. For verification purposes, sample data from Washington State, US, and UK Environment Agency are analyzed. Compared to benchmark models, the proposed VMD-ESM-GM methodology not only obtains a satisfactory prediction result for e-waste data but also predicts the future fluctuation trend of e-waste. These results indicate that the proposed VMD-ESM-GM methodology based on the decomposition-ensemble principle is a suitable model for the prediction of the e-waste quantity and could help decision-makers develop both e-waste recycling plans and circular economy plans.

Forecasting annual electricity consumption in China by employing a conformable fractional grey model in opposite direction

Electric power makes a significant contribution to economic development. Predicting annual electricity consumption is becoming increasingly crucial for electric power utility planning and economic development. To address this problem, a novel conformable fractional grey model in opposite direction is presented to predict annual electricity consumption in China. Firstly, the computational formulas for the novel model are deduced by grey modelling method and the effectiveness of the novel model is proved by matrix perturbation theory. Secondly, the optimal parameters are determined by quantum inspired evolutionary algorithm. Thirdly, two empirical examples are taken to validate the prediction accuracy of the novel model. Finally, the proposed model is applied to predict electricity consumption of Beijing, Fujian and Shandong. The results show that the novel model is superior to other six competitive models. Besides, electricity consumption of these regions in next five years are predicted, which can well serve a benchmark research and provide a relatively reliable reference for economic and electric sectors.

Predictive analysis of electronic waste for reverse logistics operations: a comparison of improved univariate grey models

Growing rates of innovation and consumer demand resulted in rapid accumulation of waste of electrical and electronic equipment or electronic waste (e-waste). In order to build and sustain green cities, efficient management of e-waste rises as a viable response to this accumulation. Accurate e-waste predictions that municipalities can utilize to build appropriate reverse logistics infrastructures gain significance as collecting, recycling and disposing the e-waste become more complex and unpredictable. In line with its significance, the related literature presents several methodologies focusing on e-waste generation forecasting. Among these methodologies, grey modeling approach has aroused interest due to its ability to present meaningful results with small-sized or limited data. In order to improve the overall success rate of the approach, several grey modeling-based forecasting techniques have been proposed throughout the past years. The performance of these models, however, profoundly leans on the parameters used with no established consensus regarding the suitable criteria for better accuracy. To address this issue and to provide a guideline for academicians and practitioners, this paper presents a comparative analysis of most utilized grey modeling methods in the literature improved by particle swarm optimization. A case study employing e-waste data from Washington State is provided to demonstrate the comparative analysis proposed in the study.

New Intelligent Control Strategy Hybrid Grey–RCMAC Algorithm for Ocean Wave Power Generation Systems

In this article, the characteristics of the wave energy converter are considered and a novel dynamic controller (NDC) for a permanent magnet synchronous generator (PMSG) is proposed for Wells turbine applications. The proposed NDC includes a recursive cerebellum model articulation controller (RCMAC) with a grey predictor and innovative particle swarm optimization (IPSO). IPSO is developed to adjust the learning speed and improve learning capability. Based on the supervised learning method, online adjustment law of RCMAC parameters is derived to ensure the system’s stability. The NDC scheme is designed to maintain a supply–demand balance between intermittent power generation and grid power supply. The proposed NDC exhibits an improved power regulation and dynamic performance of the wave energy system under various operation conditions. Furthermore, better results are obtained when the RCMAC is used with the grey predictive model method.

Application of Multi-variable Grey Model Method in Power Load Forecasting

Electric power has become one of the most important energy sources in today’s society. It is of great significance for power grid enterprises and people’s livelihood to accurately grasp the historical data of power load and to mine the potential value of data. In this paper, by making using of the multi-variable grey model, the power load forecasting is studied. Firstly, this paper establishes the multi-variable grey model with grey system theory. Secondly, the correlation between four temperature factors and load is analyzed, which shows that four temperature factors should be considered as the influencing factors of load. Finally, multi-variable grey model is applied to forecast the summer load of some region in Ningbo. The results show that the prediction has better fitting and prediction accuracy.