Title :
Fault-signature modeling and detection of inner-race bearing faults
Author :
Stack, Jason R. ; Habetler, Thomas G. ; Harley, Ronald G.
Author_Institution :
Signal & Image Process., Naval Surface Warfare Center Panama City, FL, USA
Abstract :
This paper develops a fault-signature model and a fault-detection scheme for using machine vibration to detect inner-race defects. To motivate this research, it is explained and illustrated with experimental results why fault signatures from nonouter-race defects (e.g., inner-race defects) can be less salient than those from outer-race defects. Then, a signal model is presented for the production and propagation of an inner-race fault signature; this model is then used to design an inner-race fault-detection scheme. This scheme examines machine-vibration spectra for peaks with phase-coupled sidebands occurring at a spacing predicted by the model. The proficiency of this fault-detection scheme at detecting inner-race bearing faults is then experimentally verified with results from 12 bearings representing varying degrees of fault severity.
Keywords :
electrical faults; fault diagnosis; machine bearings; machine testing; vibrations; fault signature modeling; inner-race bearing faults detection; inner-race defects detection; machine vibration spectra; Artificial neural networks; Condition monitoring; Electric machines; Fault detection; Fault diagnosis; Frequency; Predictive models; Production; Signal design; Vibrations; Amplitude modulation (AM); bearings (mechanical); condition monitoring; fault diagnosis; vibration;
Journal_Title :
Industry Applications, IEEE Transactions on
DOI :
10.1109/TIA.2005.861365