Signal Optimization and Noise Reduction Based on Frequency Control in Acoustic Imaging Instruments

Papermaking machinery noise is the noise generated during the production process of a papermaking enterprise, which includes mechanical vibration and low-frequency noise. In the field test of paper-making enterprises, the noise range is 100–120 dB. According to the industry noise standard, the value of the noise can not be greater than 85 dB under the working condition of 8 hours. Therefore, to control the source of the noise, it is necessary to control the noise according to the noise mechanism of the equipment itself and block its propagation path in order to effectively reduce the noise of the factory. Visual acoustic imagers can quickly identify the sound source and introduce corresponding noise reduction measures in line with environmental protection. The aim of this study is to investigate the frequency control based signal optimization and noise reduction methods in acoustic imagers. U sing the Visual Acoustic Imager of CRY2600 series, the authors conducted experiments in mechanical air noise and collision vibration noise environments to evaluate the effects of different noise environments, filter types, dynamic frequency tuning strategies and signal processing algorithms on signal optimization and noise reduction. The results show that both low-pass and high-pass filters are effective in improving the signal-to-noise ratio in a specific frequency range, and the dynamic frequency tuning strategy performs better in improving the signal quality compared to the static tuning strategy. Both wavelet threshold denoising and generative adversarial network denoising algorithms perform well in different noise environments (wavelet threshold denoising improves the signal strength from 50dB to 70dB in a mechanical air noise environment, with a 20dB improvement in the signal-to-noise ratio), significantly improving the signal strength. The research results show that combining multiple noise reduction strategies can effectively improve the overall performance of the acoustic imaging system, which provides new ideas for industrial noise pollution control and the development of imaging technology.