Contribution of dynamic modeling to prognostics of rotating machinery

E. Madar, R. Klein, J. Bortman

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Prognostics of rotating machines parts is an important subject in the maintenance field. The paper focuses on the contribution of the dynamic models for the development of reliable prognostics and diagnostics algorithms. The dynamic models enable prediction of changes in dynamic behavior reflecting the fault type and severity. New condition indicators and signal processing techniques can be developed based on these insights. The models can be used to build databases of simulated signals that can be used to perform sensitivity analysis with respect to geometrical tolerances, environmental and operating conditions, severity, type, and location of faults. A research methodology towards physics-based prognostics of rotating machinery is proposed. The methodology combines experiments with comprehensive dynamic models, and it is demonstrated with examples of rolling-element bearings, gear transmissions, and cardan joints. It is important to well validate the models and to assure their completeness. Therefore, experiments and models are complementary: the experimental results should be used to validate the model assumptions, and the models enable comprehensive interpretation of the test results. The combination of the two allow the generalization of the conclusions and algorithms with a high level of confidence.

Original languageEnglish
Pages (from-to)496-512
Number of pages17
JournalMechanical Systems and Signal Processing
Volume123
DOIs
StatePublished - 15 May 2019

Keywords

  • Bearings
  • Cardan joints
  • Dynamic modeling
  • Gears
  • Prognostics

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Civil and Structural Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Contribution of dynamic modeling to prognostics of rotating machinery'. Together they form a unique fingerprint.

Cite this