SUBJECT: M.S. Thesis Presentation
BY: Eymard Prevost
TIME: Wednesday, July 3, 2019, 10:00 a.m.
PLACE: Love Building, 210
TITLE: Detection of bearing defects with approximate bearing configuration
COMMITTEE: Dr. Thomas Kurfess, Chair (ME)
Dr. Christopher Saldana (ME)
Dr. Kate Fu (ME)


Unscheduled maintenance in a production line due to breakdowns is highly detrimental. The ability to predict impending failure and
anticipate it is a high value proposition. Such a prediction can be achieved by monitoring components that are known to fail often in
mechanical systems, such as bearings. Prior research has led to the development of bearing monitoring approaches widely used today.
However, one of the main challenges is the fact that there is often incomplete information about the systems. This thesis will focus on
approaches that can be employed to detect bearing defects and incipient bearing failure in the presence of incomplete and inaccurate
system knowledge.