This thesis presents a method to detect structural damage using frequencyresponsefunction (FRF) data obtained from non-destructive vibration tests. The method is used to study cases of early structural damage in whi...
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This thesis presents a method to detect structural damage using frequencyresponsefunction (FRF) data obtained from non-destructive vibration tests. The method is used to study cases of early structural damage in which there is no appreciable change in mass and damping. The resulting change in structural stiffness matrix is reflected in changes of FRF which can be exemplified by the evaluation of the damage location vector. This requires the dynamic stiffness matrix of the original undamaged structure and the frequencyresponse curve of the currently damaged structure. In this thesis, the former is obtained from a finite element model of the virgin structure and the latter are obtained from an impact hammer test. The Damage Detection Algorithm will be used to detect simulated damage FRF data applied to a simple mass spring system, space truss structure and a plate structure and effects of noise. Both numerically simulated and experimentally measured noises are investigated. Experimental FRF data were obtained for a plate structure and used for detection of physical damage and comparisons made with the simulated data. Results show that the Damage Detection Algorithm can be used to successfully detect structure damage in situations where coordinate incompatibility and noise exists.
The ladder network parameter identification for transformer winding is crucial for the interpretation of the frequency response function data. The traditional identification method, mainly based on intelligent optimis...
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The ladder network parameter identification for transformer winding is crucial for the interpretation of the frequency response function data. The traditional identification method, mainly based on intelligent optimisation algorithm, is generally very time-consuming due to a large amount of computation. This study proposes to combine the intelligent algorithm and Gauss-Newton iteration algorithm (GNIA) to improve the optimisation efficiency notably with a sharply dropped calculation workload. These two methods are well-complementary since the intelligent algorithm holds excellent global search ability while the search of the GNIA is directional and quantitative. This study solves three key problems for the combined algorithms. The first problem is the calculation of the least-square correction solution to the network parameters in the iteration algorithm. The treatment of the ill-conditioned Jacobian matrix in the iteration algorithm is the second challenge. Another issue is the determination of the network parameter with zero sensitivity. The identification results on an isolated winding show that the combined algorithms can obtain a more precise solution with far less amount of computation.
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