Sound quality evaluation and prediction for the emitted noise of axial piston pumps

Abstract

Sound quality has been an important attribute closely related to products’ competitiveness. A neural network model is proposed to predict the sound quality of an axial piston pump based on the objective and subjective sound quality evaluations. Five psychoacoustic metrics of the objective evaluation and six adjective pairs of semantic evaluation method are utilized. The influences of the speed and outlet pressure on the objective and subjective evaluation results are compared. The correlations between the objective and subjective evaluation results are analyzed. The results show that the A-weighted sound pressure levels (AWSPLs) and loudness of the sound take on increasing with the increase of the speed and outlet pressure. There are strong correlations between the AWSPL and loudness and the perceptions of “unpleasant-pleasant” and “like-dislike”, while the correlations between the fluctuation strength and the perception of “unpleasant-pleasant” and “like-dislike” are moderate, and the correlations between the roughness and sharpness and the perception of “unpleasant-pleasant” and “like-dislike” are weak. In addition, the results show that the sound quality model can predict the sound quality of axial piston pumps with good performance. This study lays the foundation for sound quality improvement of hydraulic displacement pumps.