In order to study the influence of yarn bundle vibration characteristics on the vibration and noise of tufted carpet looms, a yarn bundle vibration model was proposed in this paper, which was based on the viscoelasticity of the yarn bundle, and the correctness of the transverse vibration equation of the yarn bundle was verified by experiments. Different creep models of the yarn bundle were fitted with the experimental data, and the transverse vibration equation of the axial motion viscoelastic yarn bundle was established by using Burgers four-element constitutive model. Then, the Galerkin truncation method was used to solve the partial differential vibration equation of the yarn bundle and solve the equation. Finally, the correctness of the vibration equation is verified by comparison between the experimental results and the numerical simulation results. The results show that the vibration equation is suitable for studying the transverse dynamic vibration characteristics of the yarn bundle.

In order to recognise the noise source of a warp knitting machine, a method based on Modified Ensemble Empirical Mode Decomposition (MEEMD) and Akaike Information Criterion (AIC) is proposed. The MEEMD_AIC method is applied to measure the noise signal of a warp knitting machine and analyse every single effective component selected. Noise source identification is realised by combining the vibration signal characteristics of the main parts of the warp knitting machine. Firstly, MEEMD is used to decompose the measured noise signal of the warp knitting machine into a finite number of intrinsic mode function (IMF) components. Then, singular value decomposition (SVD) is performed on the covariance matrix of the component matrix to get the eigen value of the matrix. Next, the number of effective components is estimated based on the AIC criterion, and the effective components are selected by combining the energy characteristic index and the Pearson correlation coefficient method. The results show that the noise signal of the warp knitting machine is a mixture of multiple noise source signals. The main noise sources of the warp knitting machine, including the vibration of the pulling roller, the main shaft of the loop forming mechanism and the push rod of the guide bar traverse the mechanism, provide theoretical support for recognition of the active noise reduction of the warp knitting machine using the MEEMD_AIC method.

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