APPLICATION OF DISCRETE WAVELET TRANSFORM FOR DETECTION OF BALL BEARING RACE FAULTS

By S. Prabhakar, A. Mohanty, A. Sekhar. 2002

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Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods (2007, background): “Some have classified bearing conditions and fault diagnosis using fuzzy logic concepts [6], and neural networks approaches [7–9], while others have focused on wavelet analysis approaches to detect bearing faults [2,3,10,11].”
Fault diagnosis of ball bearings using machine learning methods (2011, background): “Prabhakar, Mohanty, and Sekhar (2002) have considered single and multiple point defects on inner race, outer race and the combination faults and used discrete wavelet transform (DWT) to detect these faults on bearings.”
A novel method for machine performance degradation assessment based on fixed cycle features test (2009, methodology): “Some examples using the data under the fixed working load can be found in the literature such as ball bearing race fault detection [10], faults diagnosis for induction motor [11] and chiller faults diagnosis [12] and so on.”
Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition (2009, methodology): “Though many wavelet functions have been developed until now, Morlet wavelet [10] and Daubechies [7,9–11] wavelet were mostly used to extract fault signatures.”