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Fault Classification and Fault Signature Production for Rolling Element Bearings in Electric Machines

Stack, J.R., Habetler, T.G., Harley, R.G.·2004·IEEE Transactions on Industry Applications

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 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.

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Metadata

Authors
Stack, J. R., Habetler, T. G., & Harley, R. G.
Venue
IEEE Transactions on Industry Applications
Published
2004-05
Volume
40
Issue
3
Pages
735-739
ISSN
0093-9994 (ISSN-L)
OpenAlex
W1985965778
Cited by
290
References
0
FWCI
16.41
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Fault Classification and Fault Signature Production for Rolling Element Bearings in Electric Machines. https://app.cochat.ai/papers/10.1109/tia.2004.827454
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Stack, J. R., Habetler, T. G., & Harley, R. G. (2004). Fault Classification and Fault Signature Production for Rolling Element Bearings in Electric Machines. IEEE Transactions on Industry Applications. https://doi.org/10.1109/tia.2004.827454

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.