Abstract
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.
Highlights
ANSI [1] stipulates that, for single-level noise, continuous noise exposure shall not exceed 80 dB when working for more than 8 hours
complete ensemble EMD with adaptive noise (CEEMDAN) has higher accuracy, and it is more suitable for the noise source extraction for the textile industry. e CEEMDAN algorithm is combined with the Akaike information criterion (AIC) source number estimation method, and the CEEMDANAIC method is for the noise source identification of the carpet tufting machine. en, the CEEMDAN-AIC method is applied for the identification of the noise source of a carpet tufting machine, and its main noise source is accurately identified. is can provide theoretical support for the active noise reduction of the carpet tufting machine
In this paper, using the CMEEMDAN-AIC algorithm combined with the carpet tufting machine structure characteristics and related experimental analysis, the noise sources of the carpet tufting machine are identified, concluded as follows: (1) A CEEMDAN-AIC algorithm which is applied to the noise source identification of the carpet tufting machine is presented
Summary
ANSI [1] stipulates that, for single-level noise, continuous noise exposure shall not exceed 80 dB when working for more than 8 hours. As an empirical signal analysis method, empirical mode decomposition (EMD) overcomes the limitation of Fourier transform fundamentally and can theoretically decompose any signal into IMFs [11,12,13,14]. Marcelo and Gaston [17] presented complete ensemble EMD with adaptive noise (CEEMDAN) It can solve mode mixing and reduce calculation with negligible reconstruction error [18,19,20]. E CEEMDAN algorithm is combined with the Akaike information criterion (AIC) source number estimation method, and the CEEMDANAIC method is for the noise source identification of the carpet tufting machine. En, the CEEMDAN-AIC method is applied for the identification of the noise source of a carpet tufting machine, and its main noise source is accurately identified. CEEMDAN has higher accuracy, and it is more suitable for the noise source extraction for the textile industry. e CEEMDAN algorithm is combined with the Akaike information criterion (AIC) source number estimation method, and the CEEMDANAIC method is for the noise source identification of the carpet tufting machine. en, the CEEMDAN-AIC method is applied for the identification of the noise source of a carpet tufting machine, and its main noise source is accurately identified. is can provide theoretical support for the active noise reduction of the carpet tufting machine
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