Bearing monitoring

Roger Xu; Mark W. Stevenson; Chi-Man Kwan; Leonard S. Haynes

doi:10.1117/12.434241

20 July 2001 Bearing monitoring

Roger Xu, Mark W. Stevenson, Chi-Man Kwan, Leonard S. Haynes

Proceedings Volume 4389, Component and Systems Diagnostics, Prognosis, and Health Management; (2001) https://doi.org/10.1117/12.434241
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

At Ford Motor Company, thrust bearing in drill motors is often damaged by metal chips. Since the vibration frequency is several Hz only, it is very difficult to use accelerometers to pick up the vibration signals. Under the support of Ford and NASA, we propose to use a piezo film as a sensor to pick up the slow vibrations of the bearing. Then a neural net based fault detection algorithm is applied to differentiate normal bearing from bad bearing. The first step involves a Fast Fourier Transform which essentially extracts the significant frequency components in the sensor. Then Principal Component Analysis is used to further reduce the dimension of the frequency components by extracting the principal features inside the frequency components. The features can then be used to indicate the status of bearing. Experimental results are very encouraging.

Citation Download Citation

Roger Xu, Mark W. Stevenson, Chi-Man Kwan, and Leonard S. Haynes "Bearing monitoring", Proc. SPIE 4389, Component and Systems Diagnostics, Prognosis, and Health Management, (20 July 2001); https://doi.org/10.1117/12.434241

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