Contribution of dynamic modeling to prognostics of rotating machinery

• Published in: Mechanical systems and signal processing Vol. 123; pp. 496 - 512
• Main Authors: Madar, E., Klein, R., Bortman, J.
• Format: Journal Article
• Language: English
• Published: Berlin Elsevier Ltd 15.05.2019; Elsevier BV
• Subjects: Algorithms; Bearings; Cardan joints; Computer simulation; Dynamic modeling; Dynamic models; Fault location; Gears; Prognostics; Roller bearings; Rotating machinery; Rotating machines; Rotation; Sensitivity analysis; Signal processing; Tolerances; Gears; Cardan joints; Bearings; Prognostics; Dynamic modeling
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2019.01.003

•Validated, comprehensive dynamic models.•Modeling contribution in diagnostics of bearings, gears and cardan joints.•Physics-based prognostics.•Insights on behavior of faulty bearings, gears and cardan joints. 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.