scholarly journals Integrated Study of Time-Frequency Representations and Their Applications in Source Identification of Mechanical Noise.

2002 ◽  
Vol 45 (3) ◽  
pp. 665-672 ◽  
Author(s):  
Min-chun PAN
Author(s):  
Min-chun Pan

Abstract Three computation schemes of time-frequency representations (TFRs) have been developed and implemented to identify different components of mechanical noise originated from the transmission system of electrical vehicles. This study explores the close relationships between three TFRs, i.e. the spectrogram based on windowed Fourier transform (WFT), the Wigner-Ville distribution (WVD), and the smoothed WVD (SWVD). One main purpose is to pursue the efficiency of computing the SWVD of a dynamic signature. The revised scheme can tremendously reduce the computation time to a scale of around 1/90, compared with the original scheme. To assess the validation of these TFR schemes, firstly, four synthetic signals are designed and processed. Secondly, the developed TFRs are applied to distinguish different spectral components of transmission noise, and identify their sources. This study takes an electrical scooter with a continuous velocity transmission (CVT) system as a test bench. The CVT-belt noise, helical-gear whine noise, and fan noise can be clearly identified via the processing of the TFRs. These obtained conclusions can be used as references for machine element modification to improve annoying noise.


Author(s):  
Weihai Sun ◽  
Lemei Han

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.


1997 ◽  
Vol 117 (3) ◽  
pp. 338-345 ◽  
Author(s):  
Masatake Kawada ◽  
Masakazu Wada ◽  
Zen-Ichiro Kawasaki ◽  
Kenji Matsu-ura ◽  
Makoto Kawasaki

2009 ◽  
Vol E92-B (12) ◽  
pp. 3717-3725
Author(s):  
Thomas HUNZIKER ◽  
Ziyang JU ◽  
Dirk DAHLHAUS

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