Rolling element bearing failure can cause problems for industries ranging from mild inconveniences such as simple replacement to catastrophic damage such as large production-line equipment failure. Rolling element bearing failure has plagued industries for many years. Bearings are currently monitored to determine whether or not there is a defect in the bearing, but the remaining lifetime of the bearing remains unknown. This research estimates the bearings remaining lifetime through digital signal processing in conjunction with a modified version of Pariss equationa fatigue-failure equation well known in rotating machinery prognostics.
An energy quantity, coined the Power Spectrum Value (PSV), is the maximum amplitude of the frequencies within a relatively small band around the resonant frequency of the system. The current PSV is estimated and updated using a chronologically weighted least squares algorithm. It is this PSV which is implemented in the modified Paris equation to determine the remaining lifetime of the bearing. This research presents a non-intrusive method of determining the lifetime of the bearing so that the bearings utility is maximized and reactive maintenance procedures are minimized.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/7568 |
| Date | 28 November 2005 |
| Creators | Lindsay, Tara Reeves |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | 2205757 bytes, application/pdf |
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