To early detect symptoms of defective rolling element bearings, this paper introduces discrete wavelet packet transform (DWPT)-based sub-band analysis. The objective of this analysis is to explore the impacts of mul...
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To early detect symptoms of defective rolling element bearings, this paper introduces discrete wavelet packet transform (DWPT)-based sub-band analysis. The objective of this analysis is to explore the impacts of multiple sub-band signals by 4-level DWPTusing proper Daubechies mother wavelet on a 2.5-second acoustic emission signal. In particular, the DWPT-based sub-bandanalysis determines the most informative sub-band signal involving intrinsic information about bearing defects among theaforementioned multiple sub-band signals based on the ratio of spectral magnitudes at harmonics of the bearing's characteristicfrequency to those around the harmonics. This paper also verifies the efficacy of the DWPT-based sub-band analysis for seededbearing defects (i.e., a crack on the inner race, the outer race, or a roller).
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