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Collaborative Prognostics in Social Asset Networks

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Abstract

With the spread of Internet of Things (IoT) technologies, assets have acquired communication, processing and sensing capabilities. In response, the field of Asset Management has moved from fleet-wide failure models to individualised asset prognostics. Individualised models are seldom truly distributed, and often fail to capitalise the processing power of the asset fleet. This leads to hardly scalable machine learning centralised models that often must find a compromise between accuracy and computational power. In order to overcome this, we present a novel theoretical approach to collaborative prognostics within the Social Internet of Things. We introduce the concept of Social Asset Networks, defined as networks of cooperating assets with sensing, communicating and computing capabilities. In the proposed approach, the information obtained from the medium by means of sensors is synthesised into a Health Indicator, which determines the state of the asset. The Health Indicator of each asset evolves according to an equation determined by a triplet of parameters. Assets are given the form of the equation but they are not aware of their parametric values. To obtain these values, assets use the equation in order to perform a non-linear least squares fit of their Health Indicator data. Using these estimated parameters, they are interconnected to a subset of collaborating assets by means of a similarity metric. We show how by simply interchanging their estimates, networked assets are able to precisely determine their Health Indicator dynamics and reduce maintenance costs. This is done in real time, with no centralised library, and without the need for extensive historical data. We compare Social Asset Networks with the typical self-learning and fleet-wide approaches, and show that Social Asset Networks have a faster convergence and lower cost. This study serves as a conceptual proof for the potential of collaborative prognostics for solving maintenance problems, and can be used to justify the implementation of such a system in a real industrial fleet.

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Acknowledgements

We acknowledge Bang Ming Yong and the VR team at the Institute for Manufacturing for providing the computer used to perform the simulations. This research was supported by SustainOwner (Sustainable Design and Management of Industrial Assets through Total Value and Cost of Ownership), a project sponsored by the EU Framework Programme Horizon 2020, MSCA-RISE-2014: Marie Skodowska-Curie Research and Innovation Staff Exchange (Rise) (grant agreement number 645733 “Sustain-owner” H2020-MSCA-RISE-2014).

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Authors and Affiliations

  1. Department of Engineering, Institute for Manufacturing, University of Cambridge, Cambridge, CB3 0FS, UK

    Adrià Salvador Palau & Zhenglin Liang

  2. Department of Engineering, Institute for Manufacturing, University of Cambridge, Cambridge, CB3 0FS, UK

    Ajith Kumar Parlikad

  3. Institut für Mathematik, AG Topologie, Arnimallee 7, Raum 212, 14195, Berlin, Germany

    Daniel Lütgehetmann

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  1. Adrià Salvador Palau
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  2. Zhenglin Liang
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  3. Daniel Lütgehetmann
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  4. Ajith Kumar Parlikad
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Correspondence to Adrià Salvador Palau .

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Editors and Affiliations

  1. Department of Industrial Management, School of Engineering of the University of Seville, Seville, Spain

    Adolfo Crespo Márquez

  2. Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Milan, Italy

    Marco Macchi

  3. Department of Engineering, University of Cambridge, Cambridge, UK

    Ajith Kumar Parlikad

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Salvador Palau, A., Liang, Z., Lütgehetmann, D., Parlikad, A.K. (2020). Collaborative Prognostics in Social Asset Networks. In: Crespo Márquez, A., Macchi, M., Parlikad, A. (eds) Value Based and Intelligent Asset Management. Springer, Cham. https://doi.org/10.1007/978-3-030-20704-5_15

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  • DOI: https://doi.org/10.1007/978-3-030-20704-5_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20703-8

  • Online ISBN: 978-3-030-20704-5

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