Model- and Non-model-based Damage Detection Methods Using Vibration Data Prof. Weidong ZHU (University of Maryland, Baltimore County)
Modal parameters of structures, such as natural frequencies and mode shapes, have been widely used for vibration-based structural damage detection. A model-based damage detection method that uses changes in natural frequencies to detect damage has advantages over conventional nondestructive tests in detecting various types of damage using minimum measurement data. Two major challenges associated with applications of the model-based method to practical engineering structures are addressed: accurate modeling of test structures and the development of a robust inverse algorithm to detect damage, which are defined as the forward and inverse problems associated with the damage detection method, respectively. To resolve the forward problem, new physics-based finite element modeling techniques for fillets in thin-walled beams and bolted joints are developed, so that complex structures with thin-walled beams and/or bolted joints can be accurately modeled with a reasonable model size. To resolve the inverse problem, a robust iterative algorithm using a trust-region method, called the Levenberg-Marquardt (LM) method is developed to accurately detect locations and extent of damage using a minimum number of measured natural frequencies. The LM method can ensure global convergence of iterations in solving severely under-determined system equations and deal with damage detection problems with relatively large modeling error and measurement noise. The vibration-based damage detection method developed is applied to various structures including lightning masts, a space frame structure, and a pipeline. The locations and extent of damage can be successfully detected in experimental damage detection. In the numerical simulation where there are no modeling error and measurement noise, exact locations and extent of damage can be detected.
Besides the model-based method, non-model-based methods that use vibration shapes to identify damage in beams and plates are introduced. Curvature mode shapes and continuous wavelet transforms of mode shapes of a damaged beam are compared with those from polynomial fits with proper orders to yield curvature damage indices and continuous wavelet transform damage indices, respectively, to identify damage. A non-model-based method that uses mode shapes and one that uses principal, mean and Gaussian curvature mode shapes are introduced to identify damage in plates. A new multi-scale differential geometry scheme is developed to calculate curvature mode shapes. Comparing a mode shape of a damaged plate with that from a polynomial that fits the mode shape can yield a mode shape damage index to identify damage, and comparing curvature mode shapes associated with a mode shape of a damaged plate with those from a polynomial that fits the mode shape can yield four curvature damage indices to identify damage. Two non-model-based methods that use vibration shapes measured by a continuously scanning laser Doppler vibrometer system are introduced to identify damage in beams. Spatially detailed vibration shapes can be measured by the system in a rapid and accurate manner. Curvature damage indices can be obtained using curvature vibration shapes from the demodulation method to identify damage. All of the above non-model-based methods are robust against measurement noise and do not require any a priori information of undamaged structures that are usually not available in practice.
Structural Health Monitoring of CFRP Structures Using Electromechanical Behavior Mr. Hyung Doh ROH and Young-Bin PARK (UNIST, Ulsan National Institute of Science and Technology)
Whereas the number and the fields of carbon fiber reinforced plastics (CFRPs) are vastly increasing, studies about in-situ real-times self-sensing are limited. Thus, our research group investigated the CFRP self-sensing capability using the electromechanical behaviors of CFRPs. Both elastic region and failure of CFRPs can be monitored by electrical resistance change ratio. Therefore, structural health monitoring (SHM) and prognostic health management (PHM) are feasible for both elastic deformation and delamination.
Improvement of MFL Sensing based Damage Detection and Quantification for Steel Bar NDE Dr. Ju-Won KIM, Jihwan PARK, Donghwan LEE, Gichun CHA and Seunghee PARK (Sungkyunkwan University)
For automated non-destructive evaluation (NDE) for the steel wire rope at the elevator facility, magnetic flux leakage (MFL) method was applied in this study. A multi-channel MFL sensor head was fabricated using Hall sensors and permanent magnets to adapt to the wire rope specimen. Several types of artificial damage were formed on a wire rope. The multi-channel MFL sensor head measured the magnetic flux density from damaged wire rope specimen while sensor head move. Signal processing processes including the Hilbert transform based enveloping process were performed to clarify the leakage signal by noise reduction. The processed signals were analyzed by comparing with the threshold value to detect the damage objectively. To quantify the damages, several types of damage index that utilize the relationship between the enveloped signal and the threshold were applied. Pattern recognition algorithm based on the extracted damage indexes was then applied for automated damage evaluation. Finally, proposed automated wire rope NDE method was applied to an actual wire rope at elevator in situ, to verify the field applicability.