Noise Source Identification and Exterior Noise Reduction of a Commercial Vehicle

2017 ◽  
Author(s):  
Prasad Padavala ◽  
Judsonrajkumar Thaveedu ◽  
G Senthilkumar ◽  
Jaganmohan Rao Medisetti
Keyword(s):  
Noise Reduction ◽  
Source Identification ◽  
Commercial Vehicle ◽  
Noise Source ◽  
Noise Source Identification

scholarly journals Noise Source Identification Method for a Carpet Tufting Machine Based on CEEMDAN-AIC

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wang Haohui ◽  
Sheng Xiaowei ◽  
Xu Yang
Keyword(s):  
Noise Reduction ◽  
Source Identification ◽  
Noise Source ◽  
Pearson Correlation ◽  
Noise Signal ◽  
Intrinsic Mode Functions ◽  
Characteristic Index ◽  
Noise Source Identification ◽  
Carpet Tufting Machine ◽  
The Impact

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.

Physics-informed Broadband Noise Source Identification and Prediction of an Ideally Twisted Rotor

2021 ◽  
Author(s):  
Christopher Thurman ◽  
Nikolas S. Zawodny ◽  
Nicole A. Pettingill ◽  
Leonard V. Lopes ◽  
James D. Baeder
Keyword(s):  
Source Identification ◽  
Noise Source ◽  
Broadband Noise ◽  
Noise Source Identification
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