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

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

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

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Health
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Structural health monitoring
Industry

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
Tinga, Tiedo ; Loendersloot, Richard . / Physical Model-Based Prognostics and Health Monitoring to Enable Predictive Maintenance. Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications. editor / Edwin Lughofer ; Moamar Sayed-Mouchaweh. Springer, 2019. pp. 313-353
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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. Springer, pp. 313-353. https://doi.org/10.1007/978-3-030-05645-2_11

Physical Model-Based Prognostics and Health Monitoring to Enable Predictive Maintenance. / Tinga, Tiedo ; Loendersloot, Richard .

Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications. ed. / Edwin Lughofer; Moamar Sayed-Mouchaweh. Springer, 2019. p. 313-353.

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

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