The aging of civil infrastructure and aerospace structures has led to an increased need to monitor the overall structural health. If growing damage not identified on time, it may has serious consequences, both safety related and economic. However, the complexity of large structures and the difficulty in accessing them makes the use of commonly existing conventional Non Destructive Evaluation (NDE) methods such as visual inspection and instrumental evaluation methods, impractical. An effective alternative in Structural Health Monitoring (SHM) is the use of methods that depend on Vibration-Based Damage Identification (VBDI) techniques. These methods use limited instrumentation to detect the changes in the measured modal characteristics of the structure, that is, its frequencies and mode shapes. These characteristics change with the physical properties of the structure (stiffness, mass and damping matrices) and can be used to help find the location and extent of damage. Optimal matrix update method is one of the VBDI algorithms that depends on finite element modelling (FEM) of the structure and is therefore referred to as model-based damage identification algorithm. The FRF differences method is also one of the VBDI techniques that depends on the directly measured frequency response functions data and is therefore referred to as non model-based or modal-based damage identification algorithm. However, VBDI algorithms still faces a number of challenges that have not been fully resolved. Some of these challenges are highlighted through modal tests designed to provide estimates of damage in a 3D eight-bay free-free frame. Details of tests on a healthy structure as well as on a structure in which predetermined damage has been introduced are presented. A proposed algorithm combining the aforementioned model-based and non-model based methods is introduced to improve the reliability of damage detection. The algorithm is first tested through numerical simulation to predicting damage on the basis of modal test data and the predictions are compared with the known damage.
Implementation of Electromechanical Impedance (EMI) as a technique to carry out Structural Health Monitoring (SHM) has been proved to be such effective tool in the last two decades, especially for structures made of composite material. EMI technique is the revolutionary evolution of the conventional Vibrational Based Damage Identification (VBDI) techniques. Unlike VBDI techniques, EMI technique has the privilege of employment tinny permanent piece of equipment which is known as Piezoelectric Wafer Active Sensor (PWAS). Although it has such advantage, it brings the necessity of continuous and robust diagnosis process of the PWAS used in the SHM system to ensure that output signals are caused by real variation of the modal parameters for the host structure rather than being occurred due to degradation and-or physical damage of the PWAS system itself. The study presented in this paper is focused on the self-diagnosis of PWAS used to carry out SHM via implementation of EMI technique. The main concern of this research is to study the ability of EMI technique to determine depolarization of PWAS and its influence on the performance of EMI based SHM system employed for structures made of composite plates. The used specimen (s) is / are underwent controlled excitation over desired frequency range, the imaginary part of admittance is plotted over desired frequency range. The whole process is then repeated for different levels of depolarization for the same specimen (s). The modeling process is carried out using a finite element commercial package, ANSYS v.15.0 in which multi-physics-based modeling can be used for such coupling field of piezoelectricity. MATLAB code is written to process the output data received from numerical simulations into counterpart spectrum and to calculate the statistical damage index Root Mean Square deviation (RMSD) that is used in damage detection. The calculated RMSD found to be non-zero values, which means false prediction. These results shows how important to have self-diagnosis for PAWS used in SHM system for further reliable measurements and data interrogation using EMI technique.
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