Noise source localization by applying multiple signal classification with wavelet transformation

Abstract

In an inverse acoustic problem with nearfield sources, it is important to separate multiple acoustic sources and to measure the position of each target. This paper proposes a new algorithm by applying multiple signal classification (MUSIC) to the outputs of discrete wavelet transformation with subband selection based on the entropy threshold. Some numerical experiments show that the proposed method can estimate the more precise positions than a conventional MUSIC algorithm under moderately correlated signal and relatively low signal-to-noise ratio case.

Noise source identification for industrial sewing machines based on non-linear partial least squares regression model

“…2,3 Research on noise reduction for industrial sewing machine is necessary and determining how to accurately identify and locate the dominant noise sources is the important issue which must be solved first because it is the key to effectively solve this problem. 4,5…”

“…The NSI is a research focus for researchers who are devoted to the study of noise reduction. 4–7 Many popular methods to conduct NSI have been presented, such as Shu and Liang 8 adopted hierarchy diagnosis together with coherent power spectrum analysis to identify complex noise sources of diesel engines, and Lee et al. 9 applied a microphone array to identify the noise-generation mechanisms of wind turbines.…”

“…2,3 Research on noise reduction for industrial sewing machine is necessary and determining how to accurately identify and locate the dominant noise sources is the important issue which must be solved first because it is the key to effectively solve this problem. 4,5 Noise source identification (NSI) techniques have recently been applied to reduce noise radiation from a wide range of products, including vehicles, household appliances and wind turbines. The goal of NSI is to identify the most primary noisy sources, which can be used to evaluate what design changes will most effectively improve overall noise radiation.…”

“…The NSI is a research focus for researchers who are devoted to the study of noise reduction. [4][5][6][7] Many popular methods to conduct NSI have been presented, such as Shu and Liang 8 adopted hierarchy diagnosis together with coherent power spectrum analysis to identify complex noise sources of diesel engines, and Lee et al 9 applied a microphone array to identify the noise-generation mechanisms of wind turbines. However, in these NSI methods, only the primary noisy sources are identified; no analytical model between the individual noisy sources and the overall noise radiation is established explicitly, which is useful for predicting the overall noise radiation level, while the noise levels of the individual noisy sources are changed.…”