Extracting Blade-Vortex Interaction Signals with Wavelet Analysis

An exploration of applications of a discrete implementation of the wavelet transform (WT) to the analysis of blade-vortex interaction (BVI) noise is presented. A BVI detection algorithm was developed which takes advantage of the prominence of BVI noise in certain frequency bands, or subbands. Isolated-BVI signals were constructed by detecting impulsive BVI events and extracting them from the subbands. Four metrics were tested for suitability as BVI estimators. Two of these, a root-mean-square (RMS) metric and an A-weighted spectral metric, were seen to characterize BVI noise severity, but were computationally expensive. The other two metrics were computed using the multi-scale information available from the WT. The first of these, computed from the amplitudes of BVI events in a single subband, was an effective BVI estimator and was relatively insensitive to noise. The second of these, based on the exponential behavior of the amplitudes across subbands, failed to characterize BVI noise severity for certain signals and was highly sensitive to noise.