Blade-vortex interaction detection and extraction under deep neural network-based scale feature model

Abstract

A deep neural network (DNN)-based method is proposed, which incorporates a blade-vortex interaction (BVI) aeroacoustic model and the improved Mallat-Zhong discrete wavelet transform (MZ-DWT) analysis, to detect and extract the BVI) signal. First, the optimal scale (OPS) and optimal scale vector (OPSV) features are defined based on the improved MZ-DWT to capture the dominant information of the BVI signal. Then, two types of deep neural network-based scale feature models (DNN-SFMs) are designed and trained to automatically obtain the OPS and OPSV features directly from the waveforms of the BVI signals. Finally, with the obtained OPS and OPSV features, a single-scale detector, multi-scale detector, single-scale extractor, and multi-scale extractor are derived for the BVI signal. The results of extensive experiments (BVI signals containing different types of noises are tested with each type of signal consisting of 10 000 or 9000 samples at each signal-to-noise ratio) demonstrate that the proposed detectors and extractors improve the accuracy and robustness of detection and extraction, respectively, and compared to the existing methods, the computational complexity is greatly reduced.