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Ecosystem condition is a fundamental component in the ecosystem accounting framework as part of the System of Environmental-Economic Accounting Experimental Ecosystem Accounting (SEEA EEA). Here, we develop a conceptual framework and present a practical structure for implementing ecosystem condition accounts to contribute to the revision process of the SEEA EEA, focussing on six core elements: (1) developing a common definition of ecosystem condition, (2) establishing a conceptual framing for ecosystem condition, (3) portraying the role of condition within the SEEA EEA accounting system, (4) deriving an inclusive multi-purpose approach, (5) describing the components of condition accounts and (6) developing a three-stage structure for reporting accounts. We develop a conceptual framework for an inclusive condition account, building on an ecological understanding of ecosystems upon which definitions, concepts, classifications and reporting structures were based. The framework encompasses the dual perspectives of first, the interdependencies of ecosystem composition, structure and function in maintaining ecosystem integrity and second, the capacity of ecosystems to supply services as benefits for humans. The following components of ecosystem condition accounts are recommended to provide comprehensive, consistent, repeatable and transparent accounts: (1) intrinsic and instrumental values, together with ecocentric and anthropocentric worldviews; (2) a formal typology or classification of characteristics, variables and indicators, based on selection criteria; (3) a reference condition used both to compare past, current and future levels of indicators of condition and as a basis for aggregation of indicators; and (4) a three-stage approach to compiling accounts with increasing levels of information and complexity that are appropriate for different purposes and applications. The recommended broad and inclusive scope of ecosystem condition and the demonstrated practical methods for implementation of accounts will enhance the ecosystem accounting framework and thus support a wider range of current and potential applications and users.

Agriculture has been identified as one of the main drivers of environmental degradation in the European Union (EU). It can have negative impacts on air, water, soil and biodiversity. The condition of agroecosystems is affected by soil degradation, especially by soil erosion, which reduces agroecosystems' capacity to provide essential ecosystem services. Therefore, it is necessary to explore synergies and trade-offs between pressures, ecosystem condition and services to create relevant information for policy and decision-makers to implement sustainable response actions. As part of the EU environmental policy, the Mapping and Assessment of Ecosystems and their Services (MAES) Working Group developed appropriate concepts to assess and link ecosystem condition and services. This study aims to test the indicators proposed by MAES to assess ecosystem condition and link it with the ecosystem services provision. For this, we applied a suggested operational framework developed in the context of the Biodiversity Strategies 2020 and 2030 for the integrated assessment of agroecosystems and regulating ecosystem service control of erosion rates supply at European scale. We quantified and mapped indicators for ecosystem condition, environmental and anthropogenic pressures and soil erosion control. We explored the relationships between the respective indicators and the capacity of agroecosystems to control soil erosion across the different Environmental Zones (EZ). Our results indicate that, in general, European agroecosystems have a high capacity to control soil erosion with some variations within the EZ. Supply capacity is positively, negatively and not correlated with the various pressure and condition indicators. Management and climate indicators play a significant role in the assessment of this service. These results highlight that conservational management practices are fundamental to reduce soil loss and improve agroecosystem condition. However, the design and implementation of such management practices must consider regional and local landscape characteristics, agricultural practices and the needs and opportunities of stakeholders. © 2021 The Author(s)

Advances in technology have enabled the installation of an increasing number of sensors in various mechanical systems allowing for more detailed equipment health monitoring capabilities. It is hoped the sensor data will enable development of predictive tools to prevent system failures. This work describes continued analysis of sensor data surrounding a seizure of a turbocharger within a propulsion system. The objective of the analysis was to characterize and distinguish healthy and failed states of the turbocharger. The analysis approach included mapping of multi-dimensional sensor data to a low-dimensional space using various linear and nonlinear techniques in order to highlight and visualize the underlying structure of the information. To provide some physical insight into the structure of the low-dimensional representation, the transformation plots were analyzed from the perspective of several engine signals. By overlaying operating ranges of individual sensor signals, certain regions of the mappings could be associated with distinct operational states of the engine, and several anomalies could be related to various points in the turbocharger seizure. Although the failed points did not map to an obvious outlier location in the transformations, incorporating expert domain knowledge with the data mining tools significantly enhanced the insight derived from the sensor data.

Plentiful work has been done for condition monitoring (CI) and fault diagnosis of fixed-axis gearboxes. However, still it is found that articles citing condition indicators for fault diagnosis of gearboxes are less in quantity, in academic journals, conference proceedings and technical reports. The specialty of condition indicators is to provide accurate information regarding the condition of various components at different levels of damage (initial, heavy or growing). Here, these indicators are addressed domain-wise and their characteristics are stated. The objective of this paper is to review and encapsulate this literature to provide a wide and good reference for researchers to be utilized. The structure of a fixed-axis gearbox is briefly introduced. The unique behaviors and fault characteristics of fixed-axis gearbox is recognized and studied. Investigations on the basis of statistical indicators are also summarized based on the adopted methodologies. Lastly, open problems are stated and further research prospects pointed out.

In the helicopter transmission systems, it is important to monitor and track the tooth damage evolution using lots of sensors and detection methods. This paper develops a novel approach for sensor selection based on physical model and sensitivity analysis. Firstly, a physical model of tooth damage and mesh stiffness is built. Secondly, some effective condition indicators (CIs) are presented, and the optimal CIs set is selected by comparing their test statistics according to Mann–Kendall test. Afterwards, the selected CIs are used to generate a health indicator (HI) through sen slop estimator. Then, the sensors are selected according to the monotonic relevance and sensitivity to the damage levels. Finally, the proposed method is verified by the simulation and experimental data. The results show that the approach can provide a guide for health monitoring of helicopter transmission systems, and it is effective to reduce the test cost and improve the system’s reliability.