Noise source identification of high-speed motion mechanism of textile equipment based on equivalent source method

Abstract

In order to accurately identify the noise sources of high-end textile equipment and achieve noise control, this paper takes warp knitting machine as an example to accurately locate the main voice mechanism. Firstly, the stability and accuracy of traditional equivalent source method (TRESM) and Bayesian regularization criterion method for acoustic field reconstruction in different frequency bands are simulated and verified. Secondly, the accuracy of iterative reweighted least squares (IRLS) and steepest descent iteration equivalent source method (SDIESM) in medium- and high-frequency bands is verified. The results show that TRESM and Bayesian methods are suitable for the identification of medium- and low-frequency noise sources, and IRLS and SDIESM algorithms have better adaptability to complex sound fields. Bayesian method, IRLS and SDIESM algorithm can be used to identify the noise sources of broadband warp knitting machine. The main noise sources are spindle motor, pulling roller, spindle of loop forming mechanism and push rod of comb bar transverse mechanism, which provide theoretical support for active noise reduction of loom.