This paper describes vibration based diagnostic algorithms for detecting a cage crack in a tapered roller bearing. Premature cage cracks have been found on the pinion end traction motor bearing of some locomotives. To guarantee operation safety and avoid unexpected down time and loss of use, monitoring and diagnostic algorithms have to be in place to detect the onset of a cage crack and provide continuous updates about the status of the propagating cage crack. This paper describes four diagnostic algorithms whose utilities were established for such a purpose: Envelope Synchronized Averaging, Bandwidth Weighted Demodulatian (BWD), Residual Analysis of AutoRegressive (AR) Models, and Classification Based an ARMA Models. A bearing test rig was designed and fabricated, and tests were carried out to obtain signals from normal and faulty bearings containing seeded cage cracks af different sizes. Validatians of these diagnostic algorithms were then performed with the experimental data to study their effectiveness. Classification Based on ARMA Models was found to be the most effective in detecting and updating the status of a cage crack.
Diagnostic algorithms for bearing cage crack detection
2003
9 Seiten, 4 Bilder, 1 Tabelle, 5 Quellen
Aufsatz (Konferenz)
Englisch
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