Keywords: Wavelets, Time-Frequency Analysis, Time-Scale
Analysis, Condition Monitoring, Vibration Analysis Signal Processing
In recent times enormous interest has emerged in the application
of wavelets, and they have been successfully implemented into
many fields of endeavour ranging from data compression and signal
processing through to the more mathematically pure field of solving
partial differential equations. It is the intention of this site
to provide the reader with a fundamental understanding of the
concepts behind wavelets, and the expanding horizon of potential
applications with an emphasis on machine condition monitoring.
In addition to this it provides a wealth of links to other informative
sites on wavelets, as well as MATLAB software available over the
internet that can provide researchers in this field an invaluable
global pool of knowledge.
Wavelets provide an alternative approach to traditional signal
processing techniques such as Fourier analysis for breaking a
signal up into its constituent parts. The driving impetus behind
wavelet analysis is their property of being localised in time
(space) as well as scale (frequency). This provides a time-scale
map of a signal, enabling the extraction of features that vary
in time. This makes wavelets an ideal tool for analysing signals
of a transient or non-stationary nature.
Signal Processing: A Historical Perspective
Non-Stationary Signal Analysis
Parametric Spectral Estimation
This 1996 tutorial, written by Joshua Altmann, was kindly given to the wavelets community March 2000 by by J. Stecki and his colleages from Monash University.