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    The MANTIS Book

    Cyber Physical System Based Proactive Collaborative Maintenance

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    Contributor(s)
    Albano, Michele (editor)
    Zurutuza, Urko (editor)
    Language
    English
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    Abstract
    In recent years, a considerable amount of effort has been devoted, both in industry and academia, to improving maintenance. Time is a critical factor in maintenance, and efforts are placed to monitor, analyze, and visualize machine or asset data in order to anticipate to any possible failure, prevent damage, and save costs. The MANTIS Book aims to highlight the underpinning fundamentals of Condition-Based Maintenance related conceptual ideas, an overall idea of preventive maintenance, the economic impact and technical solution. The core content of this book describes the outcome of the Cyber-Physical System based Proactive Collaborative Maintenance project, also known as MANTIS, and funded by EU ECSEL Joint Undertaking under Grant Agreement nº 662189. The ambition has been to support the creation of a maintenance-oriented reference architecture that support the maintenance data lifecycle, to enable the use of novel kinds of maintenance strategies for industrial machinery. The key enabler has been the fine blend of collecting data through Cyber-Physical Systems, and the usage of machine learning techniques and advanced visualization for the enhanced monitoring of the machines. Topics discussed include, in the context of maintenance: Cyber-Physical Systems, Communication Middleware, Machine Learning, Advanced Visualization, Business Models, Future Trends. An important focus of the book is the application of the techniques in real world context, and in fact all the work is driven by the pilots, all of them centered on real machines and factories. This book is suitable for industrial and maintenance managers that want to implement a new strategy for maintenance in their companies. It should give readers a basic idea on the first steps to implementing a maintenance-oriented platform or information system.
    URI
    https://library.oapen.org/handle/20.500.12657/59746
    Keywords
    Data mining; Energy
    DOI
    10.1201/9781003339748
    ISBN
    9781000794465, 9788793609853, 9781003339748, 9781000794465
    Publisher
    Taylor & Francis
    Publisher website
    https://taylorandfrancis.com/
    Publication date and place
    2018
    Grantor
    • European Commission - [...]
    Imprint
    River Publishers
    Classification
    Data mining
    Energy
    Pages
    624
    Rights
    http://creativecommons.org/licenses/by-nc/4.0
    • Imported or submitted locally

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    Credits

    • logo Scoss
    • logo EU
    • logo Scoss
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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