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Industrial Digitisation and Maintenance: Present and Future

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Predictive Maintenance in Smart Factories

Part of the book series: Information Fusion and Data Science ((IFDS))

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Abstract

In recent years various maintenance strategies have been adopted to maintain industrial equipment in an operational condition. Adopted techniques include approaches based on statistics generated by equipment manufacturers, human knowledge, and intuition based on experience among others. However, techniques like those mentioned above often address only a limited set of the potential root causes, leading to unexpected breakdown or failure. As a consequence, maintenance costs were considered a financial burden that each company had to sustain. Nevertheless, as technology advances, user experience and intuition are enhanced by artificial intelligence approaches, transforming maintenance costs into a company’s strategic asset. In particular, for manufacturing industries, a large volume of data is generated on a shop floor as digitisation advances. Combining information and communication technologies (ICT) with artificial intelligence techniques may create insight over production processes, complement or support human knowledge, revealing undetected anomalies and patterns that can help predict maintenance actions. Consequently, the company yields a reduction of unexpected breakdowns, production stoppages, and production costs. The outcomes are significant but selecting an appropriate data-driven method that can generate helpful and trustworthy results is challenging. It is mainly affected by the quality of the available data and the capability to understand the process under analysis correctly. This chapter reviews architectures for data management and data-driven methodologies for enabling predictive maintenance policies. Then follows the presentation of integrated solutions for predictive analytics to conclude with the main challenges identified and future outlook.

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Acknowledgements

This research has been partially funded by the European project “SERENA—VerSatilE plug-and-play platform enabling REmote predictive mainteNAnce”  (Grant Agreement: 767561).

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Authors and Affiliations

  1. COMAU S.p.A., Grugliasco, TO, Italy

    Massimo Ippolito

  2. Laboratory for Manufacturing Systems and Automation, University of Patras, Patras, Greece

    Nikolaos Nikolakis & Sotirios Makris

  3. Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy

    Tania Cerquitelli

  4. Dell EMC Research Europe, Cork, Ireland

    Niamh O’Mahony

  5. Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Turin, Italy

    Enrico Macii

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  1. Massimo Ippolito
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  2. Nikolaos Nikolakis
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Correspondence to Nikolaos Nikolakis .

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Editors and Affiliations

  1. Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy

    Tania Cerquitelli

  2. Laboratory for Manufacturing Systems and Automation, University of Patras, Patras, Greece

    Nikolaos Nikolakis

  3. Dell EMC Research Europe, Cork, Ireland

    Niamh O’Mahony

  4. Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Turin, Italy

    Enrico Macii

  5. COMAU S.p.A., Grugliasco, Torino, Italy

    Massimo Ippolito

  6. Laboratory for Manufacturing Systems and Automation, University of Patras, Patras, Greece

    Sotirios Makris

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Ippolito, M., Nikolakis, N., Cerquitelli, T., O’Mahony, N., Makris, S., Macii, E. (2021). Industrial Digitisation and Maintenance: Present and Future. In: Cerquitelli, T., Nikolakis, N., O’Mahony, N., Macii, E., Ippolito, M., Makris, S. (eds) Predictive Maintenance in Smart Factories. Information Fusion and Data Science. Springer, Singapore. https://doi.org/10.1007/978-981-16-2940-2_1

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