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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.

In recent years, the noise reduction research of the carpet tufting machine has been developing slowly. The research gaps of the existing work mainly focus on the noise source identification for the carpet tufting machine. MEEMD (EEMD) has been proposed to apply to source recognition on textile machinery. Due to the uniqueness of the MEEMD/EEMD, it is difficult to set suitable white noise control parameters. MEEMD (EEMD) has only been tested via simulation; however, it has not been mathematically proven or evaluated. This leads to inevitable flaws in the research conclusions, and even some conclusions are wrong. The contribution of this paper is twofold. First, in order to recognize the noise source of a carpet tufting machine, a method based on complete ensemble empirical mode decomposition (CEEMDAN) and Akaike information criterion (AIC) is proposed. The CEEMDAN-AIC method is applied to measure the noise signal of a carpet tufting machine and analyzed every single effective component selected. Noise source identification is realized by combining the vibration signal characteristics of the main parts of the carpet tufting machine. CEEMDAN is used to decompose the measured noise signal of the carpet tufting machine into a finite number of intrinsic mode functions (IMFs). Then, singular value decomposition (SVD) is performed on the covariance matrix of the IMF matrix to obtain the eigenvalue. Next, the number of effective IMFs is estimated based on the AIC criterion, and the effective IMFs are selected by combining the energy characteristic index and the Pearson correlation coefficient method. Furthermore, reconstruction and comparison of the decomposed signals of MEEMD and CEEMDAN proved that CEEMDAN is effective and accurate in source recognition. The results show that the noise signal of the carpet tufting machine is a mixture of multiple noise source signals. The main noise sources of the carpet tufting machine include shock caused by the impact of the tufted needle and looped hook and vibration of the hook-driven shaft and pressure plate. It provides theoretical support for the noise reduction of the carpet tufting machine.

Noise source identification is the first key step to reduce the noise pressure level of the carpet tufting machine. For identifying the main noise sources of the carpet tufting machine, the single channel blind source separation (SCBSS) method is proposed to separate the acquired single channel noise, and the time-frequency signal analysis is applied to identify separated noise components. The SCBSS includes ensemble empirical mode decomposition (EEMD), improved Akaike information criterion (AIC) source number estimation and fast independent component analysis (FastICA). The separation method based on EEMD-AIC-FastICA not only overcomes traditional blind source separation problems that require enough test channel numbers, but also solves the problem that the number of virtual multichannel signals is unknown. Four independent components (ICs) are obtained after using the SCBSS. Combining the time-frequency analysis of the four ICs and the acquired vibration signals of six main components, the specific four noise sources can be identified. The four ICs correspond to the noise of needles, noise of hooks, noise of hook driven shaft, and noise of motor spindle, respectively.