Physics-Informed Diagnostic and Prognostic Models for Rolling Element Bearings Using Oil Debris Data

Rolling element bearings are susceptible to rolling contact fatigue failure. This poses challenges in industrial applications like wind turbines and aircraft. To address this challenge, the thesis constructs diagnostic and prognostic models due to spalling in the inner raceway. This is achieved by integrating real-time oil debris data and a comprehensive understanding of the underlying bearing degradation mechanisms. The diagnostic model incorporates spall physical information, a heuristic "Kneedle" algorithm and a Random Forest to determine the severity of the spall propagation. For prognostics, the author thoroughly evaluates the effectiveness of the particle filter and its variants. Subsequently, the enhanced version of the Auxiliary Particle Filter with Resample Move is deployed to estimate the remaining useful life. The paramount significance of this developed model lies in its adaptability to bearings of various sizes. This versatility ensures its applicability across various industrial contexts, thus offering an effective solution.

Name thesis:degree_name

Master of Applied Science (M.App.Sc.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Engineering, Aerospace

Grantor dc:publisher

Carleton University

Year dc:date.issued

2023

Author dc:creator

• Gu, Hengyangcan

Statement dc:rights

• Copyright © 2023 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, distribution to students, research and scholarship. Theses may only be shared by linking to the Carleton University Institutional Repository and no part may be copied without proper attribution to the author; no part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2023-15776

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/41056