e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.

This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.

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Prognostics of gas turbine engine: An integrated approach


Condition-based maintenance is an emerging paradigm of modern system health monitoring, where maintenance operations are based on diagnostics and prognostics. Condition-based maintenance strategies in the industry benefit from accurate predictions of the remaining useful life (RUL) of an asset in order to optimise maintenance scheduling, resources and supply chain management. Due to the substantial costs involved, small improvements in efficiency, result in the significant cost reductions for overall maintenance services as well as its impact on energy consumption and the environment. In this paper, we present a data-driven methodology combining the hierarchical Bayesian data modelling techniques with an information-theoretic direct density ratio based change point detection algorithm to address two very generic issues namely dealing with irregular events and dealing with recoverable degradation, which are often encountered in the prognosis of complex systems such as the modern gas turbine engines. Its performance is compared with that of an existing Bayesian Hierarchical Model technique and is found to be superior in typical (heterogeneous) and non-typical scenarios. First, the technique is illustrated by an example on the simulation data and later on, it is also validated on the real-world civil aerospace gas turbine fleet data. (C) 2015 Elsevier Ltd. All rights reserved.

  • GB
  • Univ_Sheffield (UK)
Data keywords
    Agriculture keywords
    • supply chain
    Data topic
    • modeling
    Document type

    Inappropriate format for Document type, expected simple value but got array, please use list format

    Institutions 10 co-publis
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      e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
      Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.