Health Assessment Framework Research Articles

Semi-supervised machinery health assessment framework via temporal broad learning system embedding manifold regularization with unlabeled data

In the era of industrial big data, data-driven methods based on deep learning have made a splash in the field of mechanical PHM. However, the performance of deep learning models is limited by the depth of networks while previous supervised learning methods can only be applied to a small amount of labeled data while most of data acquired in the field are unlabeled, resulting in overfitting and low generalization of models. To address the above issues, a semi-supervised framework via temporal broad learning system embedding manifold regularization (TBLSMR) is proposed for machinery health assessment with unlabeled data. Specifically, linear and nonlinear temporal feature information is first learned from labeled and unlabeled data by TBLS. Then the Laplacian matrix is introduced embedding manifold regularization and used to construct objective function. Meanwhile, a specific ridge regression algorithm is designed to easily calculate the weights of network. Finally, an end-to-end mapping between the feature information layer and the label layer is established. The effectiveness of the proposed method is verified by two case studies. The comparison results with traditional models show that TBLSMR obtains high prediction accuracy while achieving training consumption savings, making it a promising approach for machinery health assessment that meets industrial needs.

Soil Health Assessment and Management Framework for Water-Limited Environments: Examples from the Great Plains of the USA

Healthy soils provide the foundation for sustainable agriculture. However, soil health degradation has been a significant challenge for agricultural sustainability and environmental quality in water-limited environments, such as arid and semi-arid regions. Soils in these regions is often characterized by low soil organic matter (SOM), poor fertility, and low overall productivity, thus limiting the ability to build SOM. Soil health assessment frameworks developed for more productive, humid, temperate environments typically emphasize building SOM as a key to soil health and have identified the best management practices that are often difficult to implement in regions with water limitations. This study reviewed existing soil health assessment frameworks to assess their potential relevance for water-limited environments and highlights the need to develop a framework that links soil health with key ecosystem functions in dry climates. It also discusses management strategies for improving soil health, including tillage and residue management, organic amendments, and cropping system diversification and intensification. The assessment of indicators sensitive to water management practices could provide valuable information in designing soil health assessment frameworks for arid and semi-arid regions. The responses of soil health indicators are generally greater when multiple complementary soil health management practices are integrated, leading to the resilience and sustainability of agriculture in water-limited environments.

Health assessment framework of marine engines enabled by digital twins

The advancements in digital twins when combined with the use of the machine learning tools can facilitate the effective health assessment and diagnostics of safety critical systems. This study aims at developing a framework to address the health assessment of marine engines utilising digital twins based on first-principles. This framework follows four distinct stages, with the former two including the marine engine digital-twin set up by customising the required thermodynamic models, as well as its calibration using tests trials data representing the engine healthy conditions. In the third stage, measurements from actual operating conditions are corrected and subsequently employed to develop the digital twin representing the prevailing conditions. The fourth stage deals with the engine health assessment by assessing health metrics derived from the developed digital twins. This framework is demonstrated in a case study of a large marine four-stroke nine-cylinder propulsion engine. The results demonstrate that three cylinders are identified to be underperforming leading to an average increase of the engine Brake Specific Fuel Consumption (BSFC) by 2.1%, whereas an average decreases of 6.8% in Indicated Mean Effective Pressure (IMEP) and 6.1% in the Exhaust Gas Temperature (EGT) are exhibited for the underperforming cylinders across the entire operating envelope. The developed digital twins facilitate the effective mapping of the engine performance for the entire operating envelope under several health conditions, providing enhanced insights for the current engine health status. The advantages of the proposed framework include the use of easily obtained data, and its application to several engine types including two and four-stroke engines for both propulsion and auxiliary use.

Construction of wetland ecosystem model framework in Phoenix Mountain National Forest Park Nature Reserve in Harbin and application case studies

Context The wetland ecosystem of Harbin Fenghuangshan National Forest Park Nature Reserve is fragile. Aims To establish a scientific and reasonable model framework for dynamic monitoring and ecological health assessment of wetlands, which is conducive to early warning, protection and management of wetland ecosystems. Methods On the basis of the full consideration of energy flow, material circulation, ecological environment and interspecific relationship, this paper proposed an ecosystem model framework. The pressure–state–response (PSR) model was used to build the evaluation index system of wetland-ecosystem health in Harbin Fenghuangshan National Forest Park. The data of each indicator are extracted and standardised through the analytic hierarchy process. Key results The results showed that the ecosystem pressure index of Harbin Fenghuangshan National Forest Park wetland was 0.52, indicating that it is in a subhealth state. The degree of health was 0.603, indicating a good condition. The comprehensive response index was 0.615, which was a positive response. The comprehensive degree of health was 0.577, indicating a subhealth state. Conclusion and implications The management and protection of wetland ecosystem in Harbin Fenghuangshan National Forest Park need to be strengthened urgently.

How History Informs FDA Tobacco Regulation: Retrospective Application of a Public Health Assessment Framework to 'Low Tar' Cigarettes

How History Informs FDA Tobacco Regulation: Retrospective Application of a Public Health Assessment Framework to 'Low Tar' Cigarettes

Integrating ecosystem health diagnosis into the construction of ecological security network—A case study in Qujing City, China

With the rapid development of the social economy, habitat fragmentation is becoming more and more obvious, which seriously threatens regional ecological security and sustainable development. Understanding the ecological health status of mountainous cities is of great significance for improving the efficiency of ecological protection. However, the research on the ecosystem health and ecological network construction of mountain cities is still limited. Given the shortcomings of the research, this study constructed an ecosystem health assessment framework in Qujing City, identifying ecological sources with the help of health diagnosis results, simulating biological walking paths with circuit theory, and identifying ecological “nodes” and barrier points. The results showed that: (1) From 2000 to 2020, the ecosystem health level of Qujing City increased first and then decreased. The ecological risk spillover phenomenon existed in the urban center area, which was strongly affected by human activities. (2) Based on VORE model and landscape connectivity software consensus, 27 ecological sources were mainly composed of forest and grassland, and the total area was 7068.24 km2. (3) 53 ecological corridors were identified by the circuit theory, with a length of 734.33 km, 83 ecological sandwichs, a total area of 58.59 km2, and 49 ecological barrier points, among which the ecological sandwichs mostly coincided with the river corridor. The barrier points were concentrated in the north and southeast of the study area. This study can provide a scientific reference for the ecological restoration of mountainous cities.

An Open Soil Health Assessment Framework Facilitating Sustainable Soil Management.

The crucial role of healthy soil in achieving sustainable food production and environment is increasingly recognized, as is the importance of proper assessment of soil quality. We introduce a new framework, open soil index (OSI), which integrally evaluates soil health of agricultural fields and provides recommendation for farming practices. The OSI is an open-source modular framework in which soil properties, functions, indicators and scores, and management advice are linked hierarchically. Soil health is evaluated with respect to sustainable crop production but can be extended to other ecosystem functions. The OSI leverages the existing knowledge base of agronomic research and routine laboratory data, enabling its application with limited cost. The OSI is a generic framework that can be adopted for specific regions with specific objectives. As a proof of concept, the OSI is implemented for all (>700,000) Dutch agricultural fields and illustrated with 11 pairs ("good" and "poor") of local fields and 32 fields where soil quality and crop yield have been monitored. The OSI produced reasonable evaluation for most pairs when soil physical functions were refined with on-site soil visual assessment. The soil functions are sufficiently independent and yet together reflect complex multidimensionality of soil quality. The framework can facilitate designing sustainable soil management programs by bridging regional targets to field-level actions.

Semi-supervised health assessment of pipeline systems based on optical fiber monitoring

Health assessment of pipeline systems is of deep significance for improving pipeline reliability and integrity. Traditional health assessment methods may be difficult or costly to perform on pipeline systems due to the long distances and environmental constraints of pipelines. This paper incorporates the distributed optical fiber sensor (DOFS) technique and the semi-supervised learning algorithm into the pipeline health assessment framework. Three critical problems that limit the application of DOFS in pipeline health assessments are addressed. First, an applicable damage monitoring experiment of a pipeline system is designed, which is effective in obtaining the necessary base data for data-driven modeling. Second, the correspondence between the pipeline health status and the monitored strain features is established. The experimental data are shared for public research, which is expected to solve the problem of the lack of benchmark research data in related fields. Third, considering the scarcity of labeled degradation data in pipelines, a semi-supervised denoising autoencoder model is proposed specifically for pipeline health assessment. The proposed method is demonstrated and validated using a comparative experimental case study.

A self‐adaptive phase‐segmentation and health assessment framework for point machines

AbstractHealth assessment for point machines is crucial to the safety of rail systems. The operation of the point machine is a typical multi‐stage process, each with its own characteristic features that allow a health assessment. Therefore, to segment various phases self‐adaptively is quite essential to assess the health state of the point machine. Besides, the degradation of the point machine is characterized as non‐linear. However, these issues are barely discussed when assessing the degradation degree. By converting it into a multi‐classification problem, this paper proposes a novel phase segmentation method based on dimensionless time‐domain features and characteristics of time series by utilizing the adaptive Multiclass Mahalanobis Taguchi System (aMMTS) to segment the signal self‐adaptively. Furthermore, this paper proposes a novel algorithm named Non‐linear Dynamic Time Warping (NLDTW), which modifies the conventional Dynamic Time Warping (DTW) by using a non‐linear distance to overcome the lack of global consistency in the non‐linear degradation assessment. Finally, a modified formula of confidence value is presented to assess the actual degradation degree. The efficiency and feasibility of the proposed framework have been verified by the actual data collected from the point machines of Guangzhou Metro.

A Reliable Framework for Secure Communication and Health Assessment of Soldiers in the Battlefield

Wars against the enemy are mandatory for the integrity of a nation. With the passage of time, the nature of war is being changed, to some extent from a conventional war to the network-centric warfare. The rate of soldiers death has increased in modern combat operations. The lack of secure connectivity and the health (mental and physical) of soldiers are among a few major reasons for the increased mortality. Mental health or stress level produces permanent changes in the physiological systems of a soldier and long-term diseases such as asthma, diabetes, and hypertension are activated with chronic stress. Although modern communication technologies may provide huge support in the efficient conduction of combat’s activities but it is at the cost of huge power consumption. The proposed research presents a computationally efficient and low-power framework that not only ensures secure communication but also provides a robust command and control system to monitor the mental stress, injury, and death of soldiers. In the suggested framework, the Advanced Encryption Standard (AES) is used for communication that is highly suitable for power constraint devices. A wearable sensor system is suggested that can be embedded in a soldier’s uniform to assist in the recording of real-time stress, injury, and death of soldiers. The system contains a heart-rate monitor to record variations in heartbeats, an EDA sensor for skin conductance, and a respiration sensor for respiration variations along with a holster unit for storage and transmission of biomedical signals. Furthermore, it also provides a mechanism to prevent misuse of soldiers’ communication equipment by the enemy thus ensuring more security. The experimental analysis shows that the proposed mental stress computation module achieved 83.33% accuracy. Moreover, a score-based comparison is performed with the existing techniques and it is found that the proposed mechanism outperformed its counterparts with 37.5% improvements in the features-based analysis. The flexibility, security, efficiency, and reliability of the framework make it appropriate for modern warfare scenarios.

Health Assessment of the Waterway from Chongqing to Yibin in the Upper Yangtze River, China

Ecological waterway construction and waterway health protection have become a trend and requirement of waterway development worldwide. How to assess the health status of a waterway is a fundamental concern for waterway sustainable development. This study established a comprehensive framework for health assessment of the waterway from Chongqing to Yibin in the upper reach of the Yangtze River, focusing on the coordinated development of river functions or services including navigation, flood discharge, sediment transport, water supply, self-purification, ecology, and recreation. This framework consists of a hierarchical indicator system, a weight determination method with analytic hierarchy process (AHP), an assessment model considering cask short board effect, and a sensitive analysis method. The waterway health in this river section in the periods 2016–2017 and 2018–2020 were assessed. The results showed that the river functions of navigation, flood discharge, water supply, ecology, and recreation had improved, while sediment transport had deteriorated from “Fair” to “Poor”, and self-purification remained at “Excellent” condition. The overall health of the waterway from Chongqing to Yibin has improved but remained in a “Fair” state during 2016–2020, at roughly the same healthy state as the other three waterways in the middle, middle-lower, and lower reaches. The results are conducive to understanding the health status of the whole Yangtze River waterway. They can serve as an important reference for ecological protection and development of high quality in the Yangtze River basin.

A Real-Time Assessment of Aquatic Ecological Health Using a Process-Based Model: An Example From Lake Poyang, China

Aquatic ecological health is a global challenge due to its significant impacts, and a real-time method to accurately assess the ecological health is therefore particularly useful. Based on the hydrological, water quality, meteorological, and topographic data collected from field observations and organizations, this study developed a process-based hydrodynamic and water quality model using an environmental fluid dynamics code (EFDC) model of Lake Poyang to simulate the spatiotemporal variations in water levels and the total nitrogen (TN), total phosphorus (TP), and chlorophyll a (Chl a) contents. Compared with the measured values, the models were confirmed to be reliable and acceptable. Then, to achieve a real-time assessment, the simulated results were calculated and transformed into five ordinal rating categories by using a lake ecosystem health assessment framework. Our research revealed the following results: (1) the highest and lowest 10-day water level statuses indicated that the hydrological conditions of this lake were at “good” or “excellent” levels. (2) The health levels of TN were mostly at the “fair” level or below, and most of the real-time assessment health curves of lake zones were close to the single peak type. (3) The TP contents were often at “good” levels, and the health assessment curves of the lake zones exhibited downward trends during the periods of rapid rise or retreat. (4) A fluctuating characteristic was found in the health assessment of Chl a with at least two declining peaks in all lake zones. This study demonstrated the high value of physics-based models for assessing the ecological health of aquatic ecosystems in a robust and accurate manner.

Urban watershed ecosystem health assessment and ecological management zoning based on landscape pattern and SWMM simulation: A case study of Yangmei River Basin

Urban watersheds encounter a series of problems in the urbanization process, including water quality degradation, landscape space fragmentation, and ecological degradation. Ecosystem health assessment and ecological management zoning are prerequisites for ecological restoration and watershed sustainability. In terms of ecological integrity and ecosystem services, a watershed ecosystem health assessment framework was constructed in this study. In addition, ecological health indicators were obtained on a grid scale by introducing landscape pattern models, hydrological models, and ecosystem service proxy models. The ecosystem health assessment results of the Yangmei River Basin in 1999, 2013, and 2019 were determined via indicator weighted calculation. The ecological health levels and spatial patterns of different periods were simultaneously compared to analyze the reasons for the dynamic changes in the watershed ecological health. The results showed that due to the impact of human activities, the watershed ecosystem health changed drastically in the western mountains and the southeastern living area from 1999 to 2013. During 2013–2019, the urbanization process triggered changes in the landscape structure, while watershed ecosystem services value increased, and the deterioration of the watershed ecosystem health slowed down. Overall, the watershed ecosystem health has deteriorated in the past 20 years, and the regional differentiation was enlarged. Based on the dynamic changes in the watershed ecological health, five ecological management zones were obtained to help decision-makers formulate reasonable urban watershed ecological restoration plans, ensuring long-term sustainable ecosystem services.

Evaluating the Health of Community/University Partnerships: Experiential Learning Practice with an Aim Toward the Public Good

This article looks at campus and community partner relationships, and how they can be assessed. Specifically, readers will find the results of a case study that explores the “health” of the relationship between an urban university and its local nonprofit partners. Additionally, the Community Partnership Health Assessment Framework (CPHAF) is introduced, which is a six-step process that practitioners engaged in service learning, internships aimed at the public good, and volunteer programs can use to evaluate the “health” of their relationships with community partners for community-based learning (CBL). For our current purpose, CBL includes academic service learning, internships, and volunteer work, however the CPHAF process is a general evaluation framework. A general tool is valuable because many universities have partnerships where students arrive from various programs such as volunteers, service learners, interns, or other. The discussion section provides a detailed description of the Community Partnership Health Assessment Framework (CPHAF), and the case study is provided as an example of its use.

Analysis of Major Bacteria and Diversity of Surface Soil to Discover Biomarkers Related to Soil Health.

The discovery of biomarkers for assessing soil health requires the exploration of organisms that can explain the core functions of soil and identification of species with major roles in these functions. However, identifying specific keystone markers within the soil microbiota is challenging. Next-generation sequencing (NGS)-based molecular-biological methods have revealed information on soil biodiversity; however, whether this biodiversity is related to soil health remains unclear. In this study, we performed NGS on grassland surface soil to compare the prokaryotic and eukaryotic genetic diversity to determine the chemical soil quality and examined markers associated with soil health. Microorganisms associated with the nitrogen cycle, bioremediation, plant pathogenicity, antibiotic production, and material degradation showed potential for use as markers. To propose a framework for soil health assessment, we not only used traditional indicators, such as chemical and physical measures, but also assessed metagenomics data of soil by land use to identify the major factors influencing the microbial structure in soil. Moreover, major keystone species were identified. Furthermore, the microbial genetic diversity of generally healthy surface soil, such as forests, farmland, and parks, was determined. These findings provide basic data for exploring soil health-related biomarkers.

Predictions of component Remaining Useful Lifetime Using Bayesian Neural Network

The Machine Prognostics and Health Management (PHM) are concerned with the prediction of the Remaining Useful Lifetime (RUL) of assets. Accurate real-time RUL predictions are necessary when developing an efficient predictive maintenance (PdM) framework for equipment health assessment. If correctly implemented, a PdM framework can maximize the interval between maintenance operations, minimize the cost and number of unscheduled maintenance operations, and improve overall availability of the large facilities like nuclear power plants (NPPs). This is especially important for nuclear power facilities to maximize capacity factor and reliability. In this work, we propose a data-driven approach to make predictions of both the RUL and its uncertainty using a Bayesian Neural Network (BNN). The BNN utilizes the Bayes by backprop algorithm with variational inference to estimate the posterior distribution for each trainable parameter so that the model output is also a PDF from which one can draw the mean prediction and the associated uncertainty. To learn the correlations between various time-series sensor data measurements, a time window approach is implemented with a two-stage noise filtering process for incoming sensor measurements to enhance the feature extraction and overall model performance. As a proof of concept, the NASA Commercial Modular Aero Propulsion System Simulation (C-MAPPS) datasets are utilized to assess the performance of the BNN model. The modeled system can be treated as a surrogate for turbine generators used in NPPs due to the similar mode of operation, degradation, and measurable variables. Comparisons against other state-of-the-art algorithms on the same datasets indicate that the BNN model can not only make predictions with comparable level of accuracy, but also offer the benefit of estimating uncertainty associated with the prediction. This additional uncertainty, which can be continuously updated as more measurement data are collected, can facilitate the decision-making process with a quantifiable confidence level within a PdM framework. Additional advantages of the BNN are showcased, such as providing component maintenance ranges and model executing frequency, with an example of how the BNN estimated uncertainty can be used to support the continuous predictive maintenance. A PdM framework based on a BNN will allow for utilities to make more informed decisions on the optimal time for maintenance so that the loss of revenue can be minimized from planned and unplanned maintenance outages.

An approach to assess spatio-temporal heterogeneity of rural ecosystem health: A case study in Chongqing mountainous area, China

Rural ecosystem health is an effective metrics of the stable state of rural components and subsystems referring to the ability to meet the reasonable needs of human society, self-maintain and update the ecosystem. With the development of regional ecosystem assessment, the theoretical breakthrough demand to integrate ecosystem composition integrity and ecosystem services (ES) supply–demand from the perspective of coupled human and ecosystem into ecosystem health assessment is increasing, stakeholders are interested in improving some comprehensive evaluation framework to reverse inadequate understanding of focusing on the single measurement of rural natural ecosystem status and external disturbance. This study established an improved rural ecosystem health assessment framework (REH-SD) with integrating ecological integrity and ES supply–demand from the perspective of social-economy-natural complex ecosystem, which put forward a set of assessment index system combining with SDGs17 indicators, and took Chongqing as a case study to reveal quantitatively spatiotemporal heterogeneity characteristics of rural ecosystem health. The results shown that: 1) The comprehensive score of rural ecosystem health in Chongqing from 2008 to 2018 was formed into an obvious variation evolutionary rule of gentle wave first and then rising rapidly ranging from 0.618 to 0.885, which was consistent with the trend of the physical state of rural ecosystem health (PREH); 2) The rural ecosystem health of 36 districts and counties in Chongqing increased gradually from the west Chongqing and metropolitan areas to the northeast and southeast Chongqing on the whole, showing a spatial heterogeneity of central and western collapse, northeast wings high, local protrusion, and the unhealth type (33.33%) and subhealth type (27.78%) dominated throughout the study area in 2018; 3) The improved REH-SD framework could appropriately modify the traditional evaluation results according to supply and demand relationship, the serious ecological deficit in metropolitan areas aggravated dramatically the deterioration of rural ecosystem, inversely, the REH-SD was significantly affected by the PREH in northeast and southeast Chongqing. The improved REH-SD framework was verified to be advanced comparing with the traditional evaluation method, and our results could provide significant differentiated recommendations for mountain ecological environment protection and restoration project.

Linking ecosystem services to a coastal bay ecosystem health assessment: A comparative case study between Jiaozhou Bay and Daya Bay, China

Coastal bays, situated within Earth’s critical zone, play important ecological and socioeconomic roles. This study developed an integrated ecosystem health assessment framework based on ecosystem services specific to coastal bays, particularly exploring approaches to determine critical indicators and establish a reference benchmark. The framework was applied to two typical coastal bays (Jiaozhou Bay and Daya Bay) to ascertain significant health differences, subsequent challenges and prospective management. Comparatively, Daya Bay was healthier. Daya Bay received an overall comprehensive index ecosystem health score of 69.42 (good); however, its ecosystem services scores differed significantly (supporting services = 71.34, regulating services = 87.29, provisioning services = 3.59, cultural services = 74.30), indicating severe degradation in its ecosystem food provisioning function. Jiaozhou Bay received corresponding score of 55.79 (moderate), and its ecosystem services scores were relatively lower (supporting services = 63.31, regulating service = 54.30, provisioning services = 6.18, and cultural services = 54.74), indicating widespread ecosystem degradation. The health status of coastal bays is closely associated with both natural characteristics and human interventions. The narrow bay mouth of Jiaozhou Bay, its limited enclosed seawater area, and the intensive land reclamation that has taken place since the 1950s have directly or indirectly led to the low score of most of its indicators, particularly regarding the significant reductions in its water area and shoreline. The geological advantages of Daya Bay is its large area of water and that it is open to the outer sea, which have contributed to its comparatively healthier status, wherein scores of only a few indicators were bad (i.e., biological indicators of sensitive organisms in the upper food chain). Results from this study provide important information for prospective bay management. For Jiaozhou Bay, more effort should be paid to improving ecosystem health in a comprehensive way, particularly fishing control, land reclamation control and habitat restoration. For Daya Bay, overfishing should be better controlled, while more emphasis should be paid to protecting and restoring certain moderately specific habitats and species. For effective management, we highlight that ecosystem management strategies needs to be differentiated among different bays based on their significant natural and social features. Our approach provides a practical ecosystem health assessment tool that can be applied to other coastal bays to better inform sustainable management and conservation initiatives.

Strategic Assessment of Neighbourhood Environmental Impacts on Mental Health in the Lisbon Region (Portugal): A Strategic Focus and Assessment Framework at the Local Level

Scientific evidence shows that each place/environment generates specific conditions with associated impacts on the mental health and well-being of the population. A holistic, multilevel and integrated environmental approach to mental health enhances the understanding of this phenomena, supporting the local decision-making processes to improve spatial planning of neighbourhood environments. The aim of this study is to develop a strategic assessment framework, based on four municipalities in the Lisbon Region (Portugal), that explores policy and planning initiatives capable of generating favourable neighbourhood environmental conditions for mental health while also detecting risks. Using baseline results of significant statistical associations between individuals’ perceptions of their neighbourhood environment and their mental health in the Lisbon Region, a Strategic Focus on Environmental and Mental Health Assessment framework (SEmHA) was built, by applying the methodology “Strategic Thinking for Sustainability” in Strategic Environmental Assessment, developed by Partidário in 2012. Taking into account the promotion of the population’s mental health, four critical decision factors of neighbourhood environments were identified: (1) public space quality (e.g., improving sense of place), (2) physical environment quality (e.g., low levels of noise exposure), (3) professional qualification and creation of economic activities (e.g., attracting new economic activities), and (4) services and facilities (e.g., improving access to health and education services). The proposed strategic focus and assessment framework contributes to ensuring that interventions in neighbourhood environments truly achieve community mental health benefits and reduce inequalities, thus helping policy makers to assess impacts at the local level.

长江中游城市群生态系统健康时空演变特征分析

城市群地区生态系统健康状况关乎城市群的可持续发展，科学测度城市群地区生态系统健康水平对城市群地区生态系统健康宏观调控政策制定具有重要的实践意义，然而当前生态系统健康评价仍存在不足之处。生态系统服务是反映生态系统健康状况的重要组成部分，如何把生态系统服务纳入生态系统健康评估框架中，成为了当前研究讨论的热点话题。拟把基于InVEST模型评估的生态系统服务纳入生态系统健康评估框架中，构建了基于"生态系统活力-生态系统组织力-生态系统弹性-生态系统服务"的评估框架，结合多源数据对1995-2015年长江中游城市群生态系统健康水平时空特征进行测度。结果显示：（1）研究期间长江中游城市群地区生态系统健康状况总体有所恶化，恶化区域主要分布在城市群的核心地区；（2）研究期间长江中游城市群地区生态系统健康存在显著的空间依赖性，生态系统健康高值区主要分布在周边山区以及中部的罗霄山脉，生态系统健康低值区主要分布在平原地区、大城市周边地区以及主要的交通线路沿线地区。本研究结果可以为其他地区生态系统健康评估以及长江中游城市群地区生态系统健康保护政策制定提供科学借鉴。;Rapid urbanization in urban agglomerations has resulted in serious degradation of ecosystem functions and deterioration of ecosystem health, which hinders the current process of new-type urbanization and ecological civilization construction in China. Healthy ecosystem in urban agglomerations is closely related to the sustainability of urban agglomerations. The scientific assessment of the ecosystem health status has important practical significance for the formulation of macro-control policies for ecosystem health in the urban agglomerations, but the current studies about the ecosystem health evaluation in urban agglomerations were still insufficient. An increasing number of studies have been carried out on the theories, concepts, spatiotemporal differentiation characteristics, and influencing factors of ecosystem health, but few studies have included the results of ecosystem services assessment based on InVEST model into the theoretical framework of ecosystem health assessment. Ecosystem services assessment is an important part to reflect the health status of the ecosystem. Ecosystem services assessment based on InVEST model can well reflect the structure, function, and ecological process of ecosystem services, and realize the spatial and dynamic quantitative assessment of ecosystem services. How to integrate ecosystem services into the framework of ecosystem health assessment has become a hot topic of the current studies. Based on the ecosystem services evaluated by InVEST model, this study constructed the ecosystem health assessment framework system of Vigor-Organization-Resilience-Ecosystem Services, and evaluated the ecosystem health level in the Middle Reaches of the Yangtze River Urban Agglomerations (MRYRUA) with multi-source data. The results showed that:(1) the ecosystem health index in Jianghan Plain, Dongting Lake Plain, Wuhan, Changsha, Nanchang, and other surrounding counties was mostly below 0.35, while the ecosystem health index of the surrounding mountainous counties was more than 0.65. In addition, the ecosystem health index of units along major traffic routes was lower than that of other regions. The overall ecosystem health status in the MRYRUA deteriorated during the study period, mainly in the core areas of these urban agglomerations. (2) The ecosystem health in the MRYRUA showed significantly spatial dependence during the study period, the high-valued areas of ecosystem health were mainly distributed in the surrounding mountainous areas and the central Luoxiao Mountains, while the low-valued areas of ecosystem health were mainly distributed in the plain areas, the surrounding areas of key cities, and the areas along the main traffic routes. The results of the study can provide scientific support for ecosystem health assessment in other regions and ecosystem health protection policy-making in the MRYRUA.