Identification of complex sound sources produced by gear units

Abstract Noise source visualisation represents an important tool for technical acoustics. Many techniques of noise source visualisation have been developed, based on a specific noise source in a specific type of acoustic environment. A new visualisation method of complex noise sources is presented, using an acoustic camera and a new algorithm. Different transient acoustical phenomena can be noted. Additionally, a new family of biorthogonal wavelets is applied to determine fault in gears. The new wavelets are a generalisation of biorthogonal wavelet systems. Smoothness is controlled independently in the analysis. For the optimisation of the synthesis bank, discrete finite variation is used. Differentiability is measured, for which a large number of vanishing wavelet moments is necessary, in favour of a smoothness measure based on the fact that a finite depth of the filter bank tree is in most case related to practical applications.

[1]  R. Douglas Martin,et al.  Smoothing and Robust Wavelet Analysis , 1994 .

[2]  Jan E. Odegard,et al.  Discrete finite variation: a new measure of smoothness for the design of wavelet basis , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[3]  Patrick Flandrin,et al.  Time-Frequency/Time-Scale Analysis , 1998 .

[4]  I. Daubechies,et al.  Biorthogonal bases of compactly supported wavelets , 1992 .

[5]  Aapo Hyvärinen,et al.  Sparse Code Shrinkage: Denoising of Nongaussian Data by Maximum Likelihood Estimation , 1999, Neural Computation.

[6]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[7]  S. Qian,et al.  Joint time-frequency analysis , 1999, IEEE Signal Process. Mag..

[8]  Peter N. Heller,et al.  The design of maximally smooth wavelets , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[9]  Truong Q. Nguyen,et al.  Wavelets and filter banks , 1996 .

[10]  Alfred Mertins,et al.  Signal Analysis: Wavelets, Filter Banks, Time-Frequency Transforms and Applications , 1999 .

[11]  I. Johnstone,et al.  Adapting to Unknown Smoothness via Wavelet Shrinkage , 1995 .

[12]  Mark J. Shensa,et al.  The discrete wavelet transform: wedding the a trous and Mallat algorithms , 1992, IEEE Trans. Signal Process..

[13]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[14]  Nouna Kettaneh,et al.  Statistical Modeling by Wavelets , 1999, Technometrics.

[15]  Peter N. Heller,et al.  Optimally smooth symmetric quadrature mirror filters for image coding , 1995, Defense, Security, and Sensing.

[16]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .