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This study presents a proposed method for assessing the condition and predicting the future status of condensers operating in seawater over an extended period. The aim is to address the problems of scaling and corrosion, which lead to increased loss of cold resources. The method involves utilising a set of multivariate feature parameters associated with the condenser as input for evaluation and trend prediction. This methodology offers a precise means of determining the optimal timing for condenser cleaning, with the ultimate goal of improving its overall performance. The proposed approach involves the integration of the analytic network process with subjective expert experience and the entropy weightmethod with objective big data analysis to develop a fusion health degreemodel. The mathematical model is constructed quantitatively using the improved Mahalanobis distance. Furthermore, a comprehensive prediction model is developed by integrating the improved Informer model and Markov error correction. This model takes into account the health status of the equipment and several influencing factors, includingmultivariate feature characteristics. This model facilitates the objective examination and prediction of the progression of equipment deterioration trends. The present study involves the computation and verification of the field time series data, which serves to demonstrate the accuracy of the condenser health-related models proposed in this research. These models effectively depict the real condition and temporal variations of the equipment, thus offering a valuable method for determining the precise cleaning time required for the condenser. [ABSTRACT FROM AUTHOR]

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IntroductionCurrently, many disasters threaten the health of ecosystems. The pressures caused by extensive human interventions and the unprincipled exploitation of natural resources have led to the degradation of natural resources and the structure and functioning of ecosystems. Hence, evaluating the consequences of human intervention in nature and meeting the growing need of humans to receive diverse services has made it necessary to evaluate the performance of watershed ecosystems in providing different services. Healthy watersheds play an important role in providing a wide variety of ecosystem services. Therefore, watershed health monitoring and evaluation is vital for the conservation of ecosystems and achieving the optimal level of services. In such a way a suitable collaborative and executive approach is established between research and watershed management. In this connection, watershed health analysis can provide valuable help in achieving the goals of integrated management of watersheds and, of course, the balance between the needs of human societies and ecosystems. However, the assessment of the health and sustainability of the watershed affected by human activities has not been sufficiently documented yet.Materials and MethodsThe current pilot research has evaluated the health of the Shiraz Darwazeh Quran Watershed in Fars Province, Iran, and the effect of existing watershed management structures in the region on the health of the watershed with a focus on floods. For this purpose, 36 key criteria from a set of climatic, anthropogenic, and hydrologic factors were identified based on the conditions of the region and the analysis of the flood occurrence process in the watershed. The selected criteria were then categorized into the three indices of pressure (P), state (S), and response (R). After screening the selected criteria in exploring the conceptual approach of pressure, state, and response (PSR), the health status was evaluated in two cases with and without watershed management structures.Results and DiscussionThe results showed that the health condition without considering the watershed management structures was at average conditions with health index of 0.55. In addition, the results of the watershed health assessment verified the non-significant effects of stone and mortar structures on improving watershed health. So that, the health conditions was remained at average status with health index of 0.53. Therefore, it can be acknowledged that the existing watershed management structures, due to the limited both number and spatial coverage, have not been able to noticeably affect the improvement of the general conditions of the watershed, despite the effect on the factors affecting the health of the sub-watersheds where the structures have been constructed. The spatial changes of the calculated variables showed that the abandoned lands, time of concentration, area under military activities, surface of sensitive formations to erosion, high density of the drainage network, density of the roads, areas of regions with high potential in producing runoff and sediment, and size of the residential areas were the most important factors affecting the health situation of the Darwazeh Quran Watershed.ConclusionAccording to the results of the analysis of the indicators and criteria considered in evaluation of the health of the Shiraz Darwazeh Quran Watershed based on the PSR approach, it was found that the watershed had an average health status. Therefore, it can be acknowledged that the existing watershed management structures have not been able to impose a noticeable effect on the prosperity and improvement of watershed health conditions. In general, it can be said that abandoned lands, short concentration time, military activities, the presence of formations sensitive to erosion, high density of drainage network and road, and the potential of high runoff and sediment production, and the size of residential areas are among the most important factors affecting the declining the health of Darwazeh Quran Watershed. Therefore, it is necessary to pay further attention to the aforesaid factors by conducting periodical monitoring with more comprehensive data, in time and also analyzing the trend of relative changes of the indicators in order to explain the adaptive and intelligent management of the watershed. The necessary measures should also be taken into account simultaneously. Therefore, it is suggested to turn the weak points of the region into strong points by applying bioengineering measures and use the opportunities in the region such as the potential of tourism and ecological features in order to improve the health status of the watershed.

In soft-soil areas, excessive transverse deformations have occurred in numerous shield tunnel linings accompanied by serious structural defects. However, to date, a method to effectively diagnose the health status of these poor-performing structures is unknown. This study focuses on health diagnosis for the typical stagger-joint-assembled shield tunnel linings in soft-soil areas of China. First, based on theoretical analyses, it is assumed that diametral deformation can be a significant indicator for the structural performance of a lining; subsequently, this is verified by abundant field data. In particular, a strong positive correlation between the diametral deformation and development of defects is observed. Subsequently, a full-scale test is performed to reveal further quantitative correlations. Besides the service limit values determined in the range of 0.24%–0.79% by specifications and prior studies, the test reveals detailed structural performance deterioration when the deformation is getting larger, where 1.5% is considered a threshold for the failure the entire structure. Additionally, the values of 0.5%, 0.8%, and 1.1% are observed to be key points indicating the gradual degradation of waterproofing, cracks, and overall mechanical performance of the lining. Based on these key points, a five-level health-degree model is constructed to determine the health status of a ring that can be regarded as a supplement of the previous limit values, which is more detailed and practical for the common large-deformed in-service linings in soft-soil areas. These findings can also serve as benchmarks for related studies on linings with different structural parameters in the future. [ABSTRACT FROM AUTHOR]

In order to realize the evaluation of the vehicle transmission system health degree, a prediction model by multi-level data fusion method is established in this paper. The prediction model applies PSO(Particle Swarm Optimization)-BP(Back Propagation) neural network algorithm, calculates the whole machine health degree and each module respective weights from the test data. On this basis, it analyzes the error between the model calculated health degree and theoretical health degree. Then the research verifies the validity and prediction model accuracy. The health degree which is obtained by the single module feature parameters fusion, and the vehicle transmission system health degree is investigated, which is less effective compared to the three-level fusions. After that, by analyzing the vehicle transmission system multi-parameter feature weights, it is found that the mechanical module accounted for the largest damage rate, and the three modules influenced the vehicle transmission system health degree in the order of mechanical module, hydraulic module, and electric control module. The study has played a guiding role in the health management of complex equipment. [ABSTRACT FROM AUTHOR]

Introduction Currently, many disasters threaten the health of ecosystems. The pressures caused by extensive human interventions and the unprincipled exploitation of natural resources have led to the degradation of natural resources and the structure and functioning of ecosystems. Hence, evaluating the consequences of human intervention in nature and meeting the growing need of humans to receive diverse services has made it necessary to evaluate the performance of watershed ecosystems in providing different services. Healthy watersheds play an important role in providing a wide variety of ecosystem services. Therefore, watershed health monitoring and evaluation is vital for the conservation of ecosystems and achieving the optimal level of services. In such a way a suitable collaborative and executive approach is established between research and watershed management. In this connection, watershed health analysis can provide valuable help in achieving the goals of integrated management of watersheds and, of course, the balance between the needs of human societies and ecosystems. However, the assessment of the health and sustainability of the watershed affected by human activities has not been sufficiently documented yet. Materials and Methods The current pilot research has evaluated the health of the Shiraz Darwazeh Quran Watershed in Fars Province, Iran, and the effect of existing watershed management structures in the region on the health of the watershed with a focus on floods. For this purpose, 36 key criteria from a set of climatic, anthropogenic, and hydrologic factors were identified based on the conditions of the region and the analysis of the flood occurrence process in the watershed. The selected criteria were then categorized into the three indices of pressure (P), state (S), and response (R). After screening the selected criteria in exploring the conceptual approach of pressure, state, and response (PSR), the health status was evaluated in two cases with and without watershed management structures. Results and Discussion The results showed that the health condition without considering the watershed management structures was at average conditions with health index of 0.55. In addition, the results of the watershed health assessment verified the non-significant effects of stone and mortar structures on improving watershed health. So that, the health conditions was remained at average status with health index of 0.53. Therefore, it can be acknowledged that the existing watershed management structures, due to the limited both number and spatial coverage, have not been able to noticeably affect the improvement of the general conditions of the watershed, despite the effect on the factors affecting the health of the sub-watersheds where the structures have been constructed. The spatial changes of the calculated variables showed that the abandoned lands, time of concentration, area under military activities, surface of sensitive formations to erosion, high density of the drainage network, density of the roads, areas of regions with high potential in producing runoff and sediment, and size of the residential areas were the most important factors affecting the health situation of the Darwazeh Quran Watershed. Conclusion According to the results of the analysis of the indicators and criteria considered in evaluation of the health of the Shiraz Darwazeh Quran Watershed based on the PSR approach, it was found that the watershed had an average health status. Therefore, it can be acknowledged that the existing watershed management structures have not been able to impose a noticeable effect on the prosperity and improvement of watershed health conditions. In general, it can be said that abandoned lands, short concentration time, military activities, the presence of formations sensitive to erosion, high density of drainage network and road, and the potential of high runoff and sediment production, and the size of residential areas are among the most important factors affecting the declining the health of Darwazeh Quran Watershed. Therefore, it is necessary to pay further attention to the aforesaid factors by conducting periodical monitoring with more comprehensive data, in time and also analyzing the trend of relative changes of the indicators in order to explain the adaptive and intelligent management of the watershed. The necessary measures should also be taken into account simultaneously. Therefore, it is suggested to turn the weak points of the region into strong points by applying bioengineering measures and use the opportunities in the region such as the potential of tourism and ecological features in order to improve the health status of the watershed. [ABSTRACT FROM AUTHOR]

Health monitoring technology is the key technology to improve the safety, combat readiness and quick response ability of equipment, which has become the necessary technology of modern high-tech equipment.In order to characterize the aircraft health status clearly and effectively, the parameters "health degree" is put forward to characterize the health level of aircraft and its sub-systems.Based on it, the calculation and determination methods of the basic health degree and the mission health degree of aircraft and its sub-systems were given out.Then, the health status evaluation methods of aircraft and its sub-systems were established based on the "health degree" parameter as well as the "healthy", "sub-healthy" and "unhealthy" employed to express the typical three healthy statuses of them.Finally, the condition-based maintenance(CBM) strategies for aircraft and its sub-systems based on the health statuses were described by means of an example:the aircraft and its sub-systems do not need maintenance when they are healthy, the aircraft and its sub-systems need to make maintenance plan when they are subhealthy, the aircraft and its sub-systems need maintenance at once when they are unhealthy, and the final implementation maintenance strategy should be carried out considering the economic requirements.The results of this paper lay a foundation for the effective applications of health monitoring technology in aircraft and its sub-systems, and they can also be extended to the health monitoring of other equipment and systems.

State-of-health (SOH) assessment for aero-engine can effectively reduce maintenance cost and operational risk, and is also a significant part of the prognostics and health management (PHM) system. However, the current SOH assessment is usually closely coordinated with other parts of PHM to achieve specific functions. This is not conducive to generalizing the function of SOH assessment. Therefore, this paper proposes a data-driven framework of SOH assessment that mainly includes data preprocessing, pseudo label generation, weight assignment and feature selection, and assessment, which enhances the systematicness of SOH assessment. A combination model based on density-distance clustering and fuzzy Bayesian risk models is designed to generate a pseudo label, select optimal parameter subset, and assign weight. Then, two assessment indicators including state membership degree and health degree are produced based on two fuzzy models for horizontal and vertical comparisons. These two indicators expand the dimensions and perspectives of SOH measurement, which can more comprehensively characterize the health state of the engine. Finally, the correctness and effectiveness of the proposed methodology are verified by the widely used Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset.

Predicting failures in Hard Disk Drives (HDD) is a major challenge that has been faced by both industry and academy in recent years. Being able to predict failure events may incur in avoiding data losses and also improve service availability. Among all failure prediction strategies, the health degree prediction is one of the most popular. The task of health degree prediction consists of, given a finite set of health states that are related to the degradation of the equipment, estimate which state reflects the actual degradation of the equipment. This problem is usually modeled as a classification task. Although many health degree prediction methods have been proposed, some practical details regarding this prediction task have been neglected in previous works. In this work we tackle two of these aspects: the ordinal nature of the problem and the different costs associated with miss-classifications. The problem can be considered as ordinal since classifying a HDD in a health level that is far from is true health level shall be more penalized than classifying in a near health level, thus a classical classification framework is not recommended. The different costs associated with mis-classifications are related to the fact that early predictions are preferred than late prediction since the later can result in failures. Such aspects are considered in a framework based on Deep Recurrent Neural Networks (DRNN). The choice of DRNN is given its remarkable performances in many applications including HDDs failure prediction. The resulting methods outperformed state-of-the-art approaches in a metric that consider the new aspects that motivated our proposal. [ABSTRACT FROM AUTHOR]

As an unmanned system, the multirotor has gained widespread attention in practical aviation engineering, because of its ease of use, reliability and maintainability. From the perspective of flight reliability and safety, it is significant and essential for a multirotor to detect anomaly occurrences and evaluate the real-time health performance status in a timely manner. Considering the limitations in the modeling of multirotor dynamics and the state estimation of existing methods, a health performance evaluation method of multirotors under wind turbulence is proposed in this paper. First, a stochastic hybrid system (SHS)-based model is established to describe the dynamic behavior of the multirotor, where the flight dynamics, external disturbances due to wind turbulence, and dynamics of discrete modes are included. The real-time probability distribution of the hybrid state of the SHS-based multirotor model is obtained by a modified interacting multiple model particle filter algorithm. Based on this, the real-time overall health performance status of the multirotor during flight is quantitatively evaluated by the classical health degree and the fuzzy health degree as indicators. Finally, a multirotor suffering from different types of sensor anomalies is simulated to demonstrate the availability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract Introduction The taxi industry has boomed over the years, both in street-hail and dispatch market. However, few studies focus on comprehensive perspectives, which make decisions such as implementing regulative or incentive policies difficult. As an economic community working in the value-oriented process, the taxi industry requires a holistic performance evaluation to determine how to adapt strategies to survive and grow. Methods This paper proposed the concept “Taxi Industry Health degree” based on the theory of Business Ecosystem and the evaluation of its health. A four-layer criteria set is developed from aspects of robustness, productivity and sustainability, and weights are determined through Analytic Hierarchy Process. Results A synthetic evaluating model combining Fuzzy Comprehensive Evaluation and Artificial Neural Network is used to maintain the goal of multi-criteria decision-making. With the GPS and Taximeter dataset of taxicabs in the whole taxi industry in Wuxi, the model is applied to empirical studies. Conclusions This paper provides sensibility analysis on not only the company’s order volatility, revenue growth and utilization of resources, but also influence on citizen’s welfare, energy consumption and environmental pollution, which enables practical regulations and policies within taxi industry.

In this paper, a novel analog circuit fault prognostic approach is presented. The Pearson product-moment correlation coefficient (PPMCC) is used to calculate the circuit's health degree on the basis of the extracted output voltages. The relevance vector machine (RVM) algorithm with kernel function optimized by the quantum-behaved particle swarm optimization (QPSO) algorithm is utilized to estimate the remaining useful performance (RUP). A leapfrog filter is used in a fault prognostic experiment to verify the prognostic approach, and the experimental results reveal that the presented approach can forecast the analog circuit's RUP precisely. [ABSTRACT FROM AUTHOR]

With the rapid development of science tourism, geoparks are brought into focus of many geologists and tourists because there are of high scientific values for experts and we can disseminate science to the public. In recent years, there have been many problems in the construction of geoparks in China. The quick demand growth of geoparks conflicts with the ecological health of the area prominently. It is of great significance for ameliorating the planning and improving the development potential of a geopark and evaluate the ecological health of a geopark by combining qualitative and quantitative indicators. Illustrated by the example of Xiaonanhai National Geopark in Qianjiang, Chongqing and based on the theory of ecological health, this paper constructs the eco-health evaluation system for geological parks through DPSIR conceptual model. We use entropy and multi-level fuzzy synthesis evaluation method to determine the factor's proportion and calculate Geopark Ecological Health Index. The result shows that the highest score of Qianjiang Xiaonanhai Geopark is 0.6643, and the lowest score is 0.6120. Though the score reveals a general trend of increase, the gap between scores of indicators is large. Based on the conclusion, some countermeasures and suggestions to improve the eco-construction and management planning of Qiannan XiaonanHai National Geopark are put forward. [ABSTRACT FROM AUTHOR]

For multicopters, failures may abort missions, crash multicopters, and moreover, injure or even kill people. In order to guarantee flight safety, a system of prognostics and health management should be designed to prevent or mitigate unsafe consequences of multicopter failures, where health evaluation is an indispensable module. This paper proposes a health evaluation method of multicopters based on Stochastic Hybrid System (SHS). In the SHS model, different working conditions (health statuses) of multicopters are modeled as discrete states, and system behaviors of different working conditions are modeled as continuous dynamics under discrete states. Then, the health of multicopters is quantitatively measured by a definition of health degree, which is a probability measure describing an extent of system degradation from an expected normal condition. On this basis, the problem of multicopter's health evaluation is transformed to a hybrid state estimation problem. In this case, a modified interacting-multiple-model algorithm is proposed to estimate the real-time distribution of hybrid state, and evaluate multicopter's health. Finally, a case study of multicopter with sensor anomalies is presented to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes two hard drive failure prediction models based on Decision Trees (DTs) and Gradient Boosted Regression Trees (GBRTs) which perform well in prediction performance as well as stability and interpretability. The models are evaluated on a real-world dataset containing 121 , 698 drives in total. Experimental results show the DT model predicts over 93% of failures at a false alarm rate under 0.01%, and the GBRT model can achieve about 90% failure detection rate without any false alarms. Moreover, the GBRT model evaluates drive health (or fault probability) which provides a quantitative indicator of failure urgency. This enables operators to allocate system resources accordingly for pre-warning migrations while maintaining the quality of user services. Aiming at practical application of prediction models, we test the models on another real-world dataset with different drive models, on a real-world hybrid dataset with multiple drive models, and on several datasets containing fewer drives. Both prediction models show steady prediction performance, with high failure detection rates (80% to 96%) and low false alarm rates (0.006% to 0.31%). We also implement a reliability model for RAID-6 systems with proactive fault tolerance and show that the proposed models can significantly improve the reliability and/or reduce construction and maintenance cost of large-scale storage systems. [ABSTRACT FROM AUTHOR]

Introduction: The taxi industry has boomed over the years, both in street-hail and dispatch market. However, few studies focus on comprehensive perspectives, which make decisions such as implementing regulative or incentive policies difficult. As an economic community working in the value-oriented process, the taxi industry requires a holistic performance evaluation to determine how to adapt strategies to survive and grow. Methods: This paper proposed the concept 'Taxi Industry Health degree' based on the theory of Business Ecosystem and the evaluation of its health. A four-layer criteria set is developed from aspects of robustness, productivity and sustainability, and weights are determined through Analytic Hierarchy Process. Results: A synthetic evaluating model combining Fuzzy Comprehensive Evaluation and Artificial Neural Network is used to maintain the goal of multi-criteria decision-making. With the GPS and Taximeter dataset of taxicabs in the whole taxi industry in Wuxi, the model is applied to empirical studies. Conclusions: This paper provides sensibility analysis on not only the company's order volatility, revenue growth and utilization of resources, but also influence on citizen's welfare, energy consumption and environmental pollution, which enables practical regulations and policies within taxi industry. [ABSTRACT FROM AUTHOR]

Recently, in order to improve reactive fault tolerance techniques in large scale storage systems, researchers have proposed various statistical and machine learning methods based on SMART attributes. Most of these studies have focused on predicting failures of hard drives, i.e., labeling the status of a hard drive as “good” or not. However, in real-world storage systems, hard drives often deteriorate gradually rather than suddenly. Correspondingly, their SMART attributes change continuously towards failure. Inspired by this observation, we introduce a novel method based on Recurrent Neural Networks (RNN) to assess the health statuses of hard drives based on the gradually changing sequential SMART attributes. Compared to a simple failure prediction method, a health status assessment is more valuable in practice because it enables technicians to schedule the recovery of different hard drives according to the level of urgency. Experiments on real-world datasets for disks of different brands and scales demonstrate that our proposed method can not only achieve a reasonable accurate health status assessment, but also achieve better failure prediction performance than previous work. [ABSTRACT FROM AUTHOR]

Abstract—This article uses normal behavior models to present an integrated approach for condition assessment of wind turbine generator systems. Monitoring parameters selected from the supervisory control and data acquisition systems of wind farms are used to establish an assessment index system. Health degree is defined to quantify indices. A neural network is used to establish normal behavior models for the parameters closely related to the natural environment. Estimation errors of the neural network models follow a normal distribution. Thus, health degrees can be calculated based on the probability density function of normal distribution. For other monitoring parameters, normal behavior models are developed based on the Parzen estimation, a common non-parametric estimation method. Health degrees are calculated based on the fitted probability density functions. Fuzzy synthetic evaluation is employed, and an assessment process is designed. Finally, the proposed method is performed on actual 1.5-MW wind turbine generator system. The results of condition assessment of two cases are compared with those obtained using the traditional method. Results show that the proposed method is more effective in assessing wind turbine generator systems than the traditional method. [ABSTRACT FROM PUBLISHER]

Water quality assessment analysis is an important technical means for water pollution prevention and control. In this research area, mechanism models and real observation data of water quality evolution are always used to perform water quality assessment. However, the existing water quality evolution mechanism modeling researches commonly use a single time-invariant model to model the water quality evolution process. It is inappropriate to directly describe the complex behavior of long-term water quality evolution with the existing models, since the evolution process contain different feature states, and the water quality evolution characteristics under these states are different. In addition, the existing water quality assessment methods are mostly methods for directly processing and calculating the observation data of water quality. This makes the existing methods difficult to effectively compensate for the contingency and randomness in the water quality evolution process, which leads to deviations and errors in performing the water quality assessment. Considering these deficiencies, this paper proposes a water quality evolution mechanism modeling and health risk assessment method based on stochastic hybrid dynamic systems (SHDS). Firstly, a hybrid water quality evolution mechanism (H-WQEM) model is established based on SHDS, and a hybrid improved fruit fly optimization algorithm (H-IFFOA) is proposed to identify the unknown parameters of the H-WQEM model. Then, an improved interacting multiple model extended Kalman filter algorithm (IIMM-EKF) is employed to estimate the probability distribution of the hybrid state of the H-WQEM model, including the probability distribution of different feature states of water bodies and the probability distribution of water quality indexes under these states. Finally, the health degree of water quality is proposed as an indicator to achieve a quantitative assessment of the water health risk status. Real observation data from a monitoring station at Baiyangwan in China is used to validate the effectiveness of the proposed method. The results show that the method can effectively describe the complex water quality evolution process, and reasonably assess the water quality health risk status. • Stochastic hybrid dynamic systems are used to model water quality evolution. • Improved fruit fly optimization algorithm is proposed to identify the model parameters. • Improved interacting multiple model filter algorithm is proposed to estimate hybrid state. • Health degree of water quality is established to achieve health risk assessment. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the theory based on multi-sensor information fusion is studied, which is used to evaluate the health condition of liquid-propellant rocket engine ground-testing bed. The concept of health degree is defined. It is used as a quantitative index for evaluating the health condition of the ground-testing bed. In order to evaluate the health condition of the ground-testing bed on different levels, health degrees of a single parameter, of a sub-system and of a system are defined. They accordingly measure the health conditions of single parameter, sub-system and system of the ground-testing bed. The method of fuzzy data fusion is used to calculate the health degree. In this method the weight of each monitoring sensor is calculated by analytic hierarchy process (AHP), then, the multi-sensor data are fused by the fuzzy comprehensive evaluation method, next, the sequence data are fused by two-grade index evaluation method, finally, the health degree is calculated by defuzzification method. Based on these, the health condition evaluation system of ground-testing bed is set up, which can evaluate the health condition of the ground-testing bed properly and quantitatively. At last, the application of the health evaluation system in fault detection and health evaluation of the ground-testing bed are discussed. [Copyright &y& Elsevier]

In this paper, to explore the current situation and future trend of the healthy development of China's sports industry, the health evaluation index system of the sports industry was established by deconstructing the health dimension of the sports industry. The health evaluation model of the sports industry was constructed by using the entropy method and the multi-objective linear weighting method. The model was used to empirically analyze the characteristics and laws of the health of China's sports industry from 2008 to 2017 and predict the health status of China's sports industry in the next 10 years with the combination with the GM (1.1) model. The results show that the potential health degree of China's sports industry exhibits a fluctuating growth trend, the actual health degree of the sports industry, and the health degree of the supporting environment in the sports industry show a steady growth trend on the whole. In the next 10 years, the health degree of China's sports industry will maintain a healthy state in which the actual health degree and potential health degree of the sports industry will enter a healthy state in 2021 and 2024, respectively. In contrast, the health degree of the supporting environment in the sports industry is always in a very unhealthy state. [ABSTRACT FROM AUTHOR]