Fuzzy Entropy Method Research Articles

Site selection of sludge pyrolysis projects based on improved interactive multi-indicators decision-making method under vague linguistic environment: A decision framework from the perspective of low-carbon

Given the escalating municipal sludge production in China, ensuring the environmental sustainability demands safe and effective treatment technology. Pyrolysis (PY) has emerged as an efficient sludge treatment technology to control the pollution as well as conserve fossil fuel. Site selection stands as a crucially task in the whole life cycle of the sludge PY project, and the researchers have launched some headway in the sludge treatment site selection. However, persistent challenges persist. For instance, the existing indicator framework remained inadequate, and the correlations among different indicators were ignored. To address these deficiencies, an improved-TODIM (Interactive Multi-Indicators Decision-Making) method has been proposed in this work. Firstly, a new assessment framework for the sludge PY site selection was established under the perspective of low-carbon. Secondly, the assessment information was described through the triangular fuzzy numbers, and the indicator weights were calculated by the combined weight of Fuzzy Analytic Hierarchy Process (FAHP) and entropy method. Furthermore, an improved TODIM method was proposed based on the Choquet integral to describe the correlation among indicators and evaluate the optional alternative. Finally, a case study in Hebei province was presented to substantiate the validity and rationality of the method.

Physiological complexity of body sway during dual-task gait in faller older adults

Background: The impact of simultaneous cognitive demand on gait’s physiological complexity in community-dwelling older adults with and without self-reported falls is yet unknown. Objective: To analyze whether the physiological complexity of body sway during dual-task and single gait is worse in once-only fallers than in non-faller older adults. Methods: A total of 58 community-dwelling older adults aged 60 to 80 participated in this study, of whom 21 had self-reported a single fall in the previous 12 months (fallers) and 37 matched participants with no fall self-reported (non-fallers). An inertial sensor (Physilog® 5, Gait Up, Switzerland) was used to acquire the time series of the body center of mass (CoM) sway in the anteroposterior (AP), mediolateral (ML) and vertical (V) directions during single gait (ST) and the gait under dual task (DT). The composite refined multiscale fuzzy entropy method was used to calculate the physiological complexity index (CI) in a MATLAB environment. SPSS (IBM; v.25.0) was used to analyze the effects of group (Fallers vs. Non-fallers), condition (ST vs. DT), and interaction (group vs. condition), using generalized linear mixed models, with an alpha of 5%. Results: No interaction or group effect was observed for the CI. However, when comparing DT with ST, a main effect of the condition was observed for the AP direction (F = 62.394; p < .001) with a reduction of 0.53 (95% CI: -0.66 to -0.39), ML (F = 4.724; p = .034) with an increase of 0.22 (95% CI: 0.17 to 0.42), and V (F = 6.457; p = .014) with a decrease of 0.17. Conclusion: Just one fall in the previous 12 months does not deleteriously influence the body’s physiological complexity during gait under concurrent cognitive demand (dual task). On the other hand, DT significantly affects the gait physiological complexity in community-dwelling older adults.

Does selective attention demand affect the physiological complexity of postural control in faller community-dwelling older adults?

Background: The physiological complexity of postural control during selective attention demand can be used as an innovative method to identify faller community-dwelling older. Objective: To analyze the effect of selective attention on the complexity of postural control in faller community-dwelling older adults. Methods: A total of 57 older adults (60 to 80 years) were divided between the faller group (Fallers n = 21) and the non-faller group (Non-fallers n = 36). An inertial sensor (Physilog® 5, GaitUp, Switzerland) was positioned over the second sacral vertebra to collect the participant’s body sway in the anteroposterior (AP) and mediolateral (ML) directions during the monochromatic and color Stroop test versions. The postural control’s complexity was calculated using MATLAB codes, employing the refined composite multiscale fuzzy entropy method. Statistical Package for Social Sciences was used to analyze the effects of interaction, group, and condition effects using linear mixed models with an alpha of 5%. Results: No significant interaction effects were observed in the AP (F = 0.18; p = 0.66) and ML (F = 0.00; p = 0.99) directions. No group effect was observed in the AP (F = 1.23; p = 0.26) and ML (F = 1.76; p = 0.18) directions. No condition effect was found in the AP (F = 0.06; p = 0.80) and ML (F = 3.54; p = 0.06) directions. Conclusion: Once-only faller community-dwelling older adults did not evidence worse physiological complexity than the non-fallers during selective attention demand in the upright standing posture.

Evaluation of tourism performance of OECD countries with integrated intuitionistic fuzzy approaches

ABSTRACT Evaluating a country’s tourism performance using different indicators and analysis methods is an important issue in strategic decision-making. In this regard, the main purpose of the study is to examine the tourism performances of OECD countries through macro tourism variables. In the research, data from 25 OECD countries for the period 2011–2020 were analysed in two periods. The intuitionistic fuzzy Entropy (IFEntropy) method was used to weigh the importance levels of the evaluation criteria. In evaluating the tourism performances, the extended intuitionistic fuzzy TOPSIS (EIFTOPSIS) and intuitionistic fuzzy CRADIS (IFCRADIS) methods were preferred. In addition, to look at the research from a different perspective, the tourism productivity of the sample countries was measured by data envelopment analysis. According to the results of the intuitionistic methods used, the 3 countries with the best tourism performance were determined as Spain, France, and Italy, respectively, and the 3 countries with the lowest performance scores were determined as Lithuania, Latvia, and Slovakia, respectively. It is understood from the results of data envelopment analysis that countries with high tourism performance cannot show the same success in the efficient use of macro tourism resources. The results obtained in the study are expected to guide decision-makers and contribute to the literature.

Assessing the Impact of Healthcare 4.0 Technologies on Healthcare Supply Chain Management: A Multi-Criteria Evaluation Framework

Background: Healthcare 4.0 has transformed supply chain management in the healthcare sector, but there is a lack of comprehensive frameworks to evaluate the impact of Healthcare 4.0 technologies on sector operations, particularly in developing countries. Methods: This study introduces a multi-criteria framework that synergically combines the techno-economic implications of Healthcare 4.0 technologies to improve healthcare supply chain management. The proposed approach innovatively integrates fuzzy VIKOR and Entropy methods to handle data vagueness and uncertainty, using data collected from healthcare supply chain specialists in Lagos, Nigeria. Results: The developed framework identifies the most and least critical technical and economic parameters for Healthcare 4.0 implementation in healthcare supply chain management. It also determines the suitability of different Healthcare 4.0 technologies for supply chain management in the healthcare sector. Conclusions: The main innovation of this study lies in the development of a comprehensive and context-specific framework for evaluating Healthcare 4.0 technologies in healthcare supply chains. The framework offers a new perspective on technology evaluation and provides practical insights for decision-makers. The findings contribute to advancing knowledge and practice in this field, promoting the proper adoption of Healthcare 4.0 technologies in healthcare, particularly in developing countries.

Assessment of polluted region using an integrated weighting approach and fuzzy VIKOR method

Air pollution exerts a profound impact on both public health and the natural environment. In India, festivals like Diwali also contaminate the air by releasing pollutants into the atmosphere. It is essential to identify the most polluted region by estimating these pollutants. Since air quality assessment involves multiple air pollutants, there may be inherent uncertainty associated with data. This study employs a fuzzy Multi Attribute Decision Making (MADM) framework fuzzy Analytical Hierarchy Process-Entropy-fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (FAHP-Entropy-FVIKOR) to model the impact of air pollution as a decision-making problem to address the uncertainty and assess the air quality during the Diwali festival from 2019 to 2021 in Tamil Nadu, India. An integrated weighting approach is utilised to determine the weights of the air pollutants using a fuzzy Analytical Hierarchy Process and Entropy methods. Mainly, the fuzzy VIKOR approach is employed to rank the polluted regions. The validation of the proposed model is established through a comparative analysis using Spearman’s rank correlation with two other existing fuzzy MADM methods. Furthermore, a sensitivity analysis is conducted to evaluate the influence of priority weights and the interdependence of pollutants in determining regional rankings. The results conclude that a strong positive correlation is attained between the proposed and existing methods and the highest levels of air pollution during the festival period are observed in Gandhi Nagar (2019), Rayapuram (2020), T. Nagar, Sowcarpet and Triplicane (2021) in their respective years. These findings substantiate the consistency and effectiveness of the proposed approach.

ωA-IvE methodology: Admissible interleaving entropy methods applied to video streaming traffic classification

This work focuses on the width-based interval fuzzy entropy notion, considering the interval data diameter as a measure of the lack of knowledge and uncertainty related to the precise membership degrees of elements in an interval-valued fuzzy set. The w-preserving notion relates the uncertainty from input to output in system information. Such a concept generates a new entropy by applying width-based average functions and admissible order to compare interval data and define width-based fuzzy connectives in data fuzzy computations. A new admissible total order is introduced, requesting just one injective and increasing function, illustrated by a Decimal Digit Interleaving (DDI) function. The proposal methods are based on the admissible interleaving order and related expressions for width-based interval entropy considering different conditions for composition among width-based interval fuzzy operators, as negations with equilibrium and average functions, also including idempotent aggregation function and restricted equivalence functions. Finally, we illustrate the application of the proposed methods for solving a video streaming traffic classification problem. The admissible interleaving entropy analysis is performed over the attributes modeling the FuzzyNetClass approach, a computational model for traffic classification related to video streaming, exploring the integration of inference systems based on interval-valued fuzzy logic and machine learning algorithms. The results by comparison with other width-based interval fuzzy entropy methods were promising and pointed to continuing study and research efforts.

Electrocardiogram morphological arrhythmia classification using fuzzy entropy-based feature selection and optimal classifier

Electrocardiogram (ECG) signal analysis has become significant in recent years as cardiac arrhythmia shares a major portion of all mortality worldwide. To detect these arrhythmias, computer-assisted algorithms play a pivotal role as beat-by-beat monitoring of holter ECG signals is required. In this paper, a morphological arrhythmia classification algorithm has been proposed to classify seven different ECG beats, namely Normal Beat (N), Left Bundle Branch Block Beat (L), Right Bundle Branch Block Beat (R), Atrial Premature Contraction Beat (A), Premature Ventricular Contraction Beat (V), Fusion of Normal and Ventricle Beat (F) and Pace Beat (P). A novel feature set of 25 attributes has been extracted from each ECG beat and ranked using the Fuzzy Entropy-based feature selection (FEBFS) technique. In addition, two distinct classifiers, support vector machine with radial basis function as the kernel (SVM-RBF) and weighted K-nearest neighbor (WKNN), are used to categorize ECG beats, and their performances are also evaluated after adjusting vital parameters. The performance of classifiers is compared for four different ECG beat segmentation approaches and further analyzed using three similarity measurement techniques and two fuzzy entropy methods while feature selection. The classifier results are also cross-validated using a 10-fold cross-validation scheme, and the MIT-BIH Arrhythmia Database has been used to validate the proposed work. After selecting 21 highly ranked features, WKNN achieves the best results with the nearest neighbor value K = 3 and cityblock distance metrics, with Average Sensitivity (Sen) = 94.89%, Positive Predictivity (Ppre) = 97.13%, Specificity (Spe) = 99.72%, F1 Score = 95.95%, and Overall Accuracy (Acc) = 99.15%. The novelty of this work relies on formulating a unique feature set, including proposed symbolic features, followed by the FEBFS technique making this algorithm efficient and reliable for morphological arrhythmia classification. The above results demonstrate that the proposed algorithm performs better than many existing state-of-the-art works.

An Integrated Approach Based on MADM and MODM for Order Picking System Considering Human Factors

Optimizing order-picking systems (OPSs) while considering human factors and integrating key decisions is a major challenge for warehouse managers. This study presents a two-stage framework based on multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) models to integrate decisions on picker selection, order batching, batch assignment, picker routing, and scheduling. In the first stage, the human factors affecting picker selection are considered as the problem’s criteria and the available pickers are treated as alternatives. The fuzzy entropy method and fuzzy COmplex PRoportional ASsessment (COPRAS) are used to weight the factors and rank the pickers, respectively. In the second stage, a three-objective mathematical model is formulated to minimize makespan and the operating costs of picking while maximizing the total scores of the selected pickers. The improved augmented epsilon constraint method (AUGMECON2) and the non-dominated sorting genetic algorithm II (NSGA-II) are applied to solve the proposed model. The performance of the two methods is tested on well-known benchmark instances and a real-world case study. The NSGA-II algorithm can generate optimal results using only about 6.58% of the CPU time required by AUGMECON2 to solve the problem. Our computational experiments show that increasing the number of pickers from 2 to 8 and doubling their capacity reduces the makespan by 2.61% and 2.74%, respectively.

Research on Evaluation of Slope Stability of Kunlun Mountain Area Highway Based on Fuzzy-Entropy method

To evaluate the stability of rocky slopes in the high-altitude, cold, and High seismic intensity terrain of the Kunlun Mountain region, we analyzed key factors influencing the stability of these slopes in relation to a specific highway project in the area. Four primary indicators, including slope morphology, meteorological and hydrological conditions, rock characteristics, and triggering factors, were selected, along with 16 secondary indicators, as fuzzy evaluation criteria. Based on the entropy weighting-fuzzy theory, we established a stability assessment model for rocky slopes in this region. Finally, taking the rocky slope section K128+540-K128+560 as an example, we used the evaluation model to calculate a 51.71% probability of slope instability. The evaluation results align with the actual on-site conditions, providing valuable insights and practical implications for analyzing and evaluating the stability of similar rocky slopes in this region and other projects.

Use of Composite Multivariate Multiscale Permutation Fuzzy Entropy to Diagnose the Faults of Rolling Bearing.

The study focuses on the fault signals of rolling bearings, which are characterized by nonlinearity, periodic impact, and low signal-to-noise ratio. The advantages of entropy calculation in analyzing time series data were combined with the high calculation accuracy of Multiscale Fuzzy Entropy (MFE) and the strong noise resistance of Multiscale Permutation Entropy (MPE), a multivariate coarse-grained form was introduced, and the coarse-grained process was improved. The Composite Multivariate Multiscale Permutation Fuzzy Entropy (CMvMPFE) method was proposed to solve the problems of low accuracy, large entropy perturbation, and information loss in the calculation process of fault feature parameters. This method extracts the fault characteristics of rolling bearings more comprehensively and accurately. The CMvMPFE method was used to calculate the entropy value of the rolling bearing experimental fault data, and Support Vector Machine (SVM) was used for fault diagnosis analysis. By comparing with MPFE, the Composite Multiscale Permutation Fuzzy Entropy (CMPFE) and the Multivariate Multiscale Permutation Fuzzy Entropy (MvMPFE) methods, the results of the calculations show that the CMvMPFE method can extract rolling bearing fault characteristics more comprehensively and accurately, and it also has good robustness.

Selection of an Insurance Company in Agriculture through Hybrid Multi-Criteria Decision-Making.

Crop insurance is used to reduce risk in agriculture. This research is focused on selecting an insurance company that provides the best policy conditions for crop insurance. A total of five insurance companies that provide crop insurance services in the Republic of Serbia were selected. To choose the insurance company that provides the best policy conditions for farmers, expert opinions were solicited. In addition, fuzzy methods were used to assess the weights of the various criteria and to evaluate insurance companies. The weight of each criterion was determined using a combined approach based on fuzzy LMAW (the logarithm methodology of additive weights) and entropy methods. Fuzzy LMAW was used to determine the weights subjectively through expert ratings, while fuzzy entropy was used to determine the weights objectively. The results of these methods showed that the price criterion received the highest weight. The selection of the insurance company was made using the fuzzy CRADIS (compromise ranking of alternatives, from distance to ideal solution) method. The results of this method showed that the insurance company DDOR offers the best conditions for crop insurance for farmers. These results were confirmed by a validation of the results and sensitivity analysis. Based on all of this, it was shown that fuzzy methods can be used in the selection of insurance companies.

Pulse Wave Measuring Sensor System via the Average Magnitude Difference Function Entropy for Classifying Cardiac Conditions

The formation and changes of the pulse wave are affected by the functional status of the heart, blood, and arterial vessels. The pulse signal covers pathological information in the cardiovascular system, and the doctor of traditional Chinese medicine (TCM) can diagnose diseases by feeling the pulse. The diagnosis of diseases through feeling the pulse of TCM primarily relies on the doctor’s feelings and subjective experience, and it lacks objectified data. In this work, pulse signals were collected digitally by designing pulse sensors and pulse acquisition systems in accordance with the feel pulse habits of TCM. The pulse rate variability was used to characterize the functional state of the heart. The average magnitude difference function (AMDF) fuzzy entropy method was proposed to investigate the change in pulse rate. A total of 77 volunteers with different heart health conditions were recruited for the collection of the pulse signal. Results showed that the AMDF fuzzy entropy can distinguish clearly among knotted pulse, regular intermittent pulse, and normal pulse. Therefore, this method has potential application value to realize cardiac-function state analysis and abnormal recognition.

Selecting features by utilizing intuitionistic fuzzy Entropy method

Feature selection is the most significant pre-processing activity, which intends to reduce the data dimensionality for enhancing the machine learning process. The evaluation of feature selection must consider classification, performance, efficiency, stability, and many factors. Nowadays, uncertainty is commonly occurred in the feature selection process due to time limitations, imprecise information, and the subjectivity of human minds. Moreover, the theory of intuitionistic fuzzy set has been proven as an extremely valuable tool to tackle the uncertainty and ambiguity that arises in many practical situations. Thus, this study introduces a novel feature selection framework using intuitionistic fuzzy entropy. In this regard, new entropy for IFS is proposed first and then compared with some of the previously developed entropy measures. As entropy is a measure of uncertainty present in data (features), features with higher entropy values are filtered out, and the remaining features having lower entropy values have been used to classify the data. To verify the effectiveness of the proposed entropy-based feature selection, some experiments are done with ten standard benchmark datasets by employing a support vector machine, K-nearest neighbor, and Naïve Bias classifiers. The outcomes of the study validate that the proposed entropy-based filter feature selection is more feasible and impressive than existing filter-based feature selection methods.

A stall diagnosis method based on entropy feature identification in axial compressors

AbstractA stall diagnosis method based on the entropy feature extraction algorithm is developed in axial compressors. The reliability of the proposed method is determined and a parametric sensitivity analysis is experimentally conducted for two different types of compressor stall diagnoses. A collection of time‐resolved pressure sensors is mounted circumferentially and along the chord direction to measure the dynamic pressure on the casing. Results show that the stall and prestall precursor embedded in the dynamic pressures are identified through nonlinear feature perturbation extraction using the entropy feature extraction algorithm. Further analysis demonstrates that the prestall precursor with the peak entropy value is related to the unsteady tip leakage flow for the spike‐type stall diagnosis. The modal wave inception with increasing amplitude is identified by the considerable increase of the entropy value. The flow field in the tip region indicates that the modal wave corresponds to the flow separation in the suction side of the rotor blade. The warning time is 100–300 rotor revolutions for both types of stall diagnoses, which is beneficial for stall control in different axial compressors. Moreover, a parametric study of the embedding dimension m, similar tolerance n, similar radius r, and data length N in the fuzzy entropy method is conducted to determine the optimal parameter setting for stall diagnosis. The stall warning based on the entropy feature extraction algorithm provides a new stall diagnosis approach in the axial compressor with different stall types. This stall warning can also be adopted as an online stability monitoring index when using the concept of active stall control.

Multi-criteria decision-making problem based on the novel probabilistic hesitant fuzzy entropy and TODIM method

The probabilistic hesitant fuzzy sets (P-HFSs) allow experts to express their preferences regarding different membership degrees with probabilistic information. Fuzzy entropy is an important concept to measure the uncertainty of probabilistic hesitant fuzzy information. In this paper, we propose a novel fuzzy entropy named as the exponential probabilistic hesitant fuzzy entropy for the P-HFEs. This novel fuzzy entropy not only observes the basic property of fuzzy entropy, but also has the same characteristics and change tends with the existing fuzzy entropy proposed and studied by other scholars. Then, based on the cardinal reconciliation method and TODIM(Tomada de decisao interativa multicriterio) method, we propose a multi-criteria decision-making (MCDM) problem, where the attribute weight is determined by the exponential probabilistic hesitant fuzzy entropy of the integration results. Finally, a application case in green building is given for the sake of illustrating the efficiency of the MCDM problem. Numerical and theoretical results show that a MCDM problem based on the proposed novel fuzzy entropy and the TODIM method have a wide range of application.

Research on internal control optimization of Financial Sharing Center Based on fuzzy mathematics theory and quality function deployment theory

Through scientific theoretical methods, we take the internal control optimization of the Financial Sharing Center of H company as the research object. Firstly, we introduce the Financial Sharing Center and the development background and research significance of internal control under this mode, sort out the existing international research and related concepts, analyze the problems existing in the internal control stage of the Financial Sharing Center, and analyze the problems one by one from the five elements of internal control. What is more innovative is that we use the quality function deployment theory in the field of system science, combined with the intuitionistic fuzzy set theory, G1 method and entropy method in fuzzy mathematics to evaluate the five elements affecting the internal control optimization of the Financial Sharing Center of H company, and give the priority of optimization in theory. Finally, according to the implementation conditions of the Financial Sharing Center, this paper puts forward relevant countermeasures and suggestions to optimize the internal control of the financial sharing mode of H company, which can also provide experience for other enterprises that are building the Financial Sharing Center.

A New Hybrid Fermatean Fuzzy Set and Entropy Method for Risk Assessment

Risk evaluation is an important part of the product design and product manufacturing process; it entails the pursuit of the highest product quality and preventing failure under the constraints of limited resources. The failure mode and effects analysis approach is one of the most widely applied risk evaluation tools that uses the product of the three risk elements of product failure items, severity, occurrence probability, and detection probability, to calculate the risk priority number, the priority of failure risk. However, the typical failure mode and effects analysis method ignores the consideration of objective weights, which may lead to incorrect evaluation results. Moreover, the method of expressing information about product failure provided by experts also directly affects the results of risk assessment. To comprehensively assess the risk of product failure, in this study, the hybrid of the Fermatean fuzzy set and entropy method was used to prioritize product failure items risk. This study used a service failure mode and effects analysis numerical example of self-service electric vehicles to illustrate and test the correctness of the proposed new hybrid Fermatean fuzzy set and entropy method. The mathematical operation results were also compared with the listing of different calculation methods. The test results prove that the proposed new hybrid Fermatean fuzzy set and entropy method can fully consider the cognitive information provided by experts to provide more accurate risk ranking results of failure items.

Sustainable prime movers selection for biogas-based combined heat and power for a community microgrid: A hybrid fuzzy multi criteria decision-making approach with consolidated ranking strategies

Combined heat and power (CHP) based community microgrid using renewable resources such as biogas is gaining fast prominence in municipalities across the world as a strategy to decarbonise energy sector and brings power supply close to the consumers. But for profitable operation of a CHP system, the selection of suitable prime movers (PMs) is very germane. It has become a significant issue to assess PM alternatives in detail from the investor’s or policy maker’s view point. Due to varying characteristics of each PM, multi criteria decision making (MCDM) techniques could be efficient tools for selecting optimal PM and making a reasonable decision. In this work, four PMs such as internal combustion engine (ICE), gas turbine (GT), micro-gas turbine (MGT) and fuel cells (FCs) are selected as alternatives with twenty evaluation criteria. The weights of criteria for each PM are evaluated taking a cue from the integration of Fuzzy-best-worst (Fuzzy-BWM) and Fuzzy-Entropy methods using fuzzy preference variables. These PMs are ranked using Fuzzy-Combined Compromise Solution (Fuzzy-CoCoSo) on the basis of technical, economic, environmental and social specifications. Thereafter, results based on each weighting preference are merged by using the Borda count and Copeland ranking strategies. The results reveal that initial investment cost in the economic category and footprint in the social category are respectively the most and least significant criteria for PM assessment. It is also revealed that FCs are the most sustainable PM with a compromise score of 2.8053 followed by ICE with a score of 2.5767. The least ranked PM is the MGT with a compromise value of 2.5541. Furthermore, the robustness and reliability of the results are tested by applying a sensitivity analysis and comparative assessment with five existing Fuzzy MCDM methods. This research could be useful to investors and policy makers for the selection of sustainable PM at the planning stage of CHP based community microgrid.

Assessing Aging Status and Type of XLPE Cable Insulation With a Graphic Approach Based on PDC Measurement

The polarization and depolarization current (PDC) measurement has been widely used to assess the aging degree of cross-linked polyethylene (XLPE) cables. However, the aging type identification of XLPE cables has not been fully considered. In this study we conduct PDC measurement on thermal-aged and water tree-aged XLPE cable samples respectively, upon which a graphical approach to assess the insulation condition and distinguish the aging type of XLPE cables is proposed. First, to designate the evaluation indexes of the insulation condition, multiple PDC measurement based dielectric parameters are considered. Then, to reflect the sensitivity of each evaluation index to the condition of XLPE cable, a combined weighting method based on the group fuzzy analytic hierarchy process (GFAHP) and entropy method (EM) is introduced. Furthermore, the improved radar chart method (RCM) based graphical approach is proposed to comprehensively and intuitively characterize the evaluation indexes. Finally, the aging degree of the XLPE cable samples is quantitatively assessed by calculating the area of the polygon in the chart. Simultaneously, utilizing the position of the areal coordinate and the shape characteristic parameter of the polygon, the thermal-aged and water tree-aged samples are effectively distinguished.