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.

Table of Contents

Wavelet Basics

Signal Processing: A Historical Perspective

Non-Stationary Signal Analysis

Synchronous Sampling

Parametric Spectral Estimation

Time-Frequency Analysis



Internet Links

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.