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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Stack, J.R. Habetler, T.G. Harley, R.G. |
| Copyright Year | 2003 |
| Description | Author affiliation: US Naval Coastal Syst. Center, Panama City, FL, USA (Stack, J.R.) |
| Abstract | Most condition monitoring techniques for rolling element bearings are designed to detect the four characteristic fault frequencies. This has lead to the common practice of categorizing bearing faults according to fault location (i.e., inner race, outer race, ball, or cage fault). While the ability to detect the four characteristic fault frequencies is necessary, this approach neglects another important class of faults that arise in many industrial settings. This research introduces the notion of categorizing bearing faults as either single-point defects or generalized roughness. These classes separate bearing faults according to the types of fault signatures that are produced rather than by the physical location of the fault. Specifically, single-point defects produce the four predictable characteristic fault frequencies while faults categorized as generalized roughness produce unpredictable broadband changes in the machine vibration and stator current. Experimental results are provided from bearings failed in situ via a shaft current. These results illustrate the unpredictable and broadband nature of the effects produced by generalized roughness bearing faults. This issue is significant because a successful bearing condition monitoring scheme must be able to reliably detect both classes of faults. |
| Sponsorship | IEEE Power Electron. Soc |
| Starting Page | 172 |
| Ending Page | 176 |
| File Size | 646826 |
| Page Count | 5 |
| File Format | |
| ISBN | 0780378385 |
| DOI | 10.1109/DEMPED.2003.1234568 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-08-24 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Condition monitoring Shafts Electric machines Fault detection Rolling bearings Production Fault location Stators Frequency Electrical fault detection |
| Content Type | Text |
| Resource Type | Article |
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