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Physical Model-Based Prognostics and Health Monitoring to Enable Predictive Maintenance

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Predictive Maintenance in Dynamic Systems

Abstract

This chapter addresses the development and application of predictive maintenance concepts for several types of assets, following two approaches: (1) detection and prediction of failures based on (real-time) monitoring the health or condition of the systems, and (2) prediction of failures (prognostics) using physical failure models and monitoring of loads or usage. Firstly, several challenges in the field of predictive maintenance are presented. These challenges will be addressed by the methods and tools discussed in the remainder of the chapter. Both the structural health monitoring methods and the prognostic concepts presented are based on a thorough understanding of the system and physical failure behaviour. After discussing the approaches for monitoring and prognostics, a series of decision support tools is presented. As a large number of methods and techniques are available, the selection of the most suitable method, as well as the critical parts in a system, is a challenging task. The presented tools assist in this selection process. Finally, the practical implementation of the presented approaches is discussed by showing a number of case studies in different sectors of industry.

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Authors and Affiliations

  1. Dynamics Based Maintenance, University of Twente, Enschede, The Netherlands

    Tiedo Tinga & Richard Loendersloot

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  1. Tiedo Tinga
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  2. Richard Loendersloot
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Correspondence to Tiedo Tinga .

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Editors and Affiliations

  1. Fuzzy Logic Laboratorium Linz-Hagenberg, Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz, Linz, Austria

    Edwin Lughofer

  2. Institute Mines-Telecom Lille Douai, Douai, France

    Moamar Sayed-Mouchaweh

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Tinga, T., Loendersloot, R. (2019). Physical Model-Based Prognostics and Health Monitoring to Enable Predictive Maintenance. In: Lughofer, E., Sayed-Mouchaweh, M. (eds) Predictive Maintenance in Dynamic Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-05645-2_11

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  • DOI: https://doi.org/10.1007/978-3-030-05645-2_11

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