Acoustic spatial patterns recognition based on convolutional neural network and acoustic visualization.

In this work, a convolutional neural network (CNN) is applied to recognize acoustic spatial patterns with the aid of acoustic visualization. The acoustic spatial patterns are obtained by the singular value decomposition of an acoustic radiation operator built with the boundary integral equation. It is to explore the powerful capability of the CNN in the image processing by analogously rendering the measured acoustic spatial patterns into images. Due to practical limitations, a higher resolution of an acoustic image is achieved by interpolating the pressure on a coarse grid. Steady-state analysis of acoustic problems is a complex domain problem. The acoustic fields are then supplied into a CNN scheme as two-channel data which are real and imaginary components of the pressure. Random noises and incident waves with varying energy are added to the measured data to simulate influences from uncorrelated and correlated noises, respectively. It is demonstrated that once the CNN scheme is built and trained with adequate data, which is numerically synthesized, the patterns can be more accurately and robustly recognized by comparing it with the cross-correlation based methods. The hierarchical feature representative as well as nonlinear perception makes the proposed method a promising approach for fault diagnosis and condition monitoring based on spatial acoustic measurements.