Noise Source Identification in Training Facilities and Gyms

This paper deals with noise problems in industrial sites adapted for commercial training venues. The room acoustics of such an object were analyzed in the scope of the reverberation time and potential acoustic adaptation measures are indicated. Identification and classification of noise sources in training facilities and gyms was carried out based on the acoustic measurements. The influence of rubber padding on impact and noise reduction was investigated in the case of chosen noise-intensive exercise activities performed in a previously described acoustic environment. Potential noise reduction measures are proposed in the form of excitation reduction, vibration isolation, and room acoustics adaptation.

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

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 assessment of taxibotted versus conventional taxiing operations using a phased microphone array

In sustainable aviation the focus is mostly applied to the greenhouse gas emissions during flight. However airports have an increasing interest in reducing emissions during ground operations such as taxiing for example to improve the local air quality. Amsterdam Airport Schiphol started a pilot for sustainable taxiing with a pilot-controlled hybrid-electric aircraft towing vehicle called TaxiBot in 2020. The COVID-19 pandemic created an opportunity for extensive operational testing on a near-empty airport. Due to the low background noise levels in this situation, also a noise assessment of taxiing with the TaxiBot versus conventional two-engine taxiing was performed. This assessment can be used to evaluate the noise levels to which ground workers or neighbouring communities are exposed due to TaxiBot operations. For the noise measurements a phased microphone array was used, which allowed not only for a noise level and directionality assessment, but also for noise source identification. This paper compares the noise emissions and noise sources between a taxibotted and conventional taxiing operation. The results show that a taxibotted taxiing operation produces significantly lower noise levels. Additionally, acoustic imaging shows that the TaxiBot engine is the main noise source for a taxibotted pass-by manoeuvre.