The Acoustic Signal Denoising Method for Rotating Machinery Based on Virtual Sample Integrating the Deep Neural Network

The clean signal is used as the reference signal in acoustic signal denoising methods based on the supervised deep learning, but the clean signal of the operating state of the rotating machinery is difficult to obtain, which leads to difficulties in constructing the denoising model. Therefore, the novel acoustic signal denoising method based on the clean signal virtual sample (CSVS) integrating the deep neural network (DNN) (CSVS-DNN) is proposed. The frequency band that contains the most fault information is selected based on the sideband characteristics of the modulation signal. Then, the CSVS dataset is generated based on the distribution of amplitude variations of the acoustic signal, the fault characteristic frequencies, and the transmission paths in the mechanical structure and air. Moreover, the generated CSVS dataset and the actual device operation signal are used to construct the acoustic signal denoising model based on the DNN. Finally, the denoising effect is evaluated using the signal-to-noise ratio (SNR) and the prominence degree coefficient (PDC) through the experimental data and the actual industrial data. The analysis results indicate that the average SNR of the proposed method is improved by at least 0.6 dB, and the average PDC is enhanced by at least 0.05 compared to other denoising methods.