The application of wavelet analysis method to civil infrastructure health monitoring

Abstract

Wavelet analysis and its applications have become one of the fastest growing research areas in the recent years. This is in part attributed to the pioneering work by the researchers as well as practitioners in the field of signal processing. Morlet first coined down the term of wavelet analysis in early 1980s. Meyer developed a wavelet basis in 1986, which is best known today as Meyer basis. Later, Mallat and Meyer formulated a theory of multiresolution analysis theory, and subsequently, proposed the Mallat algorithm, making wavelet transform readily implementable with digital computers. In 1990s, advanced research and development in wavelet analysis have found numerous applications in such areas as signal processing, image processing, and pattern recognition with many encouraging results. Despite this fast growth in theories and applications, the theoretical development of wavelet transform itself is somewhat lagging behind as compared to its applications. Recently, a new method based on wavelet analysis and wavelet transform has been developed to process nonlinear and nonstationary time series data by Huang[1,2,3,4] and J. P. Li, Y. Y. Tang [5,6]. This novel method, consisted of wavelet transform, Hilbert Spectral Analysis and Empirical Mode Decomposition. It has been applied to a variety of geophysical and bio-engineering problems. The specific application to civil infrastructure health monitoring has been reported. The basic method and infrastructure health monitoring application will be discussed here.