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Data-Driven Intelligent Predictive Maintenance of Industrial Assets

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Women in Industrial and Systems Engineering

Part of the book series: Women in Engineering and Science ((WES))

Abstract

Condition monitoring and predictive maintenance enable to focus on the operating and degradation conditions of a specific asset and designing maintenance policies that are optimal for the single asset and not for an entire fleet or a population of similar assets. While this was already possible for critical systems such as power plants for decades, decreased costs of condition monitoring solutions enabled a tight health monitoring also for less critical devices, enabling thereby an improved availability of the assets and decreased life cycle costs. With the increased accessibility to large amounts of condition monitoring data, the challenges of the developed predictive maintenance applications have also increased. The book chapter provides an introduction to predictive maintenance and the current state of knowledge in the field, particularly focusing on the data-driven approaches. It points out challenges and finally presents approaches that overcome some of the existing challenges.

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Authors and Affiliations

  1. Chair of Intelligent Maintenance Systems, ETH Zürich, Zurich, Switzerland

    Olga Fink

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  1. Olga Fink
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Correspondence to Olga Fink .

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Editors and Affiliations

  1. Department of Industrial and Systems Engineering, Auburn University, Auburn, AL, USA

    Alice E. Smith

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Fink, O. (2020). Data-Driven Intelligent Predictive Maintenance of Industrial Assets. In: Smith, A. (eds) Women in Industrial and Systems Engineering. Women in Engineering and Science. Springer, Cham. https://doi.org/10.1007/978-3-030-11866-2_25

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  • DOI: https://doi.org/10.1007/978-3-030-11866-2_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11865-5

  • Online ISBN: 978-3-030-11866-2

  • eBook Packages: EngineeringEngineering (R0)

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