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

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    2 Citations (Scopus)

    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 (realtime)
    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.
    Original languageEnglish
    Title of host publicationPredictive Maintenance in Dynamic Systems
    Subtitle of host publicationAdvanced Methods, Decision Support Tools and Real-World Applications
    EditorsEdwin Lughofer, Moamar Sayed-Mouchaweh
    PublisherSpringer
    Chapter11
    Pages313-353
    ISBN (Electronic)978-3-030-05645-2
    ISBN (Print)978-3-030-05644-5
    DOIs
    Publication statusPublished - 1 Mar 2019

    Fingerprint Dive into the research topics of 'Physical Model-Based Prognostics and Health Monitoring to Enable Predictive Maintenance'. Together they form a unique fingerprint.

  • Cite this

    Tinga, T., & Loendersloot, R. (2019). Physical Model-Based Prognostics and Health Monitoring to Enable Predictive Maintenance. In E. Lughofer, & M. Sayed-Mouchaweh (Eds.), Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications (pp. 313-353). Springer. https://doi.org/10.1007/978-3-030-05645-2_11