Due to increasing awareness of the importance of the maintenance topic in different industries, guided ultrasonic wave propagation technique has been widely used for structural health monitoring (SHM) of structures, in recent years. SHM systems are one of the most important tools for evaluating and ensuring the safety of structural systems and constructions. Several researches have been done to design SHM systems for diagnosing damages in different mechanical, aerospace and civil structures. In this thesis, guided ultrasonic wave propagation based SHM is studied. At first, SHM is introduced as an online and suitable method for the maintenance of structures, and guided wave based SHM is explained. Afterwards, signal processing of guided wave signals, as the most important step in a damage detection process, is presented. Different signal processing aspects including: pre-processing, signal processing and feature extraction, pattern recognition and making the damage detection process intelligent, are described. Next, identification of damage in a thick steel beam is studied, as a sample SHM application. By using available finite element simulation and experimental signals, identification of the location and severity of created damage is investigated. In this study, different algorithms are introduced and are used for processing the signals and determining the damage characteristics. At first, de-noising and compressing of guided wave signals by means of discrete wavelet transform (DWT) and wavelet packet transform (WPT), respectively, is studied; and the effect of choosing different orthogonal and bi-orthogonal mother wavelets in the quality of de-noising and compressing processes is investigated. The results are showing the better performance of bi-orthogonal wavelets in guided wave signals de-noising and compression. Next, an algorithm is proposed based on some general statistical features of the signals for justify; MARGIN: 0cm 0cm 0pt" Keywords: Structural health monitoring (SHM), Guided ultrasonic wave propagation, Damage identification, Signal processing, Wavelet transform (WT), De-noising, Compressing, Neural networks (NN), Support vector machines (SVM), Wavelet networks (WN), Hardware implementation, FPGA.
