Application of Wavelet Transform to Structural Health Monitoring

To extend life of infrastructures and buildings, their continuous monitoring is essential. Though structures are normally designed to last 50 and ndash;100 yr, overloads, excessive usage, exposure to extreme weather or service conditions and other unexpected factors can cause more rapid deterioration. Therefore, Structural Health Monitoring (SHM) for important structures has been actively investigated in recent years. Health monitoring enables detection of damage in structures and their timely repair and rehabilitation. This can prolong service life of structure and improve its reliability. Damage is defined as changes to the material and/or geometric properties of a structural system, including changes to the boundary conditions and system connectivity, which adversely affect the system and rsquo;s performance. The SHM process involves the observation of a system over time using periodically sampled dynamic response measurements from an array of sensors, the extraction of damage-sensitive features from these measurements, and the statistical analysis of these features to determine the current state of system health. After extreme events, such as earthquakes or blast loading, SHM is used for rapid condition screening and aims to provide, in real time information regarding the integrity of the structure. The aim of SHM is detection of damage at the early stages of growth. This leads to the necessity of developing practical and accurate nondestructive techniques for crack detection. Existence of structural damage in the system leads to alteration of vibration modes Many existing vibration-based approaches for damage detection require the modal properties with the aid of the traditional Fourier transform while other are based directly on the examination of response signal. This study intends to employ Wavelet Transform for response signal processing. Wavelet is a precise, fast evolving mathematical and signal processing tool for vibration analysis which made possible to decompose and reconstruct the measured raw data efficiently. It is a time and ndash;frequency analysis that provides more detailed information about non-stationary signals which traditional Fourier analysis cannot capture.

To extend life of infrastructures and buildings, their continuous monitoring is essential. Though structures are normally designed to last 50 and ndash;100 yr, overloads, excessive usage, exposure to extreme weather or service conditions and other unexpected factors can cause more rapid deterioration. Therefore, Structural Health Monitoring (SHM) for important structures has been actively investigated in recent years. Health monitoring enables detection of damage in structures and their timely repair and rehabilitation. This can prolong service life of structure and improve its reliability. Damage is defined as changes to the material and/or geometric properties of a structural system, including changes to the boundary conditions and system connectivity, which adversely affect the system and rsquo;s performance. The SHM process involves the observation of a system over time using periodically sampled dynamic response measurements from an array of sensors, the extraction of damage-sensitive features from these measurements, and the statistical analysis of these features to determine the current state of system health. After extreme events, such as earthquakes or blast loading, SHM is used for rapid condition screening and aims to provide, in real time information regarding the integrity of the structure. The aim of SHM is detection of damage at the early stages of growth. This leads to the necessity of developing practical and accurate nondestructive techniques for crack detection. Existence of structural damage in the system leads to alteration of vibration modes Many existing vibration-based approaches for damage detection require the modal properties with the aid of the traditional Fourier transform while other are based directly on the examination of response signal. This study intends to employ Wavelet Transform for response signal processing. Wavelet is a precise, fast evolving mathematical and signal processing tool for vibration analysis which made possible to decompose and reconstruct the measured raw data efficiently. It is a time and ndash;frequency analysis that provides more detailed information about non-stationary signals which traditional Fourier analysis cannot capture.