A hybrid particle swarmNelderMead optimization method for crack detection in cantilever beams
The estimation of a crack location and depth in a cantilever beam is formulated as an optimization problem and the optimal location and depth are found by minimizing the cost function which is based on the difference of the first four measured and calculated natural frequencies. Calculated natural frequencies are obtained using a rotational spring model of the crack, and measured natural frequencies are obtained by using cracked beam frequency response and modal analysis. A hybrid particle swarmNelderMead (PSNM) algorithm is used for estimating the crack location and depth. The hybrid PSNM is made-up of a modified particle swarm optimization algorithm (PSO), aimed to identify the most promising areas, and a NelderMead simplex algorithm (NM) for performing local search within these areas. The PSNM results are compared with those obtained by the PSO, a hybrid geneticNelderMead algorithm (GANM) and a neural network (NN). The proposed PSNM method outperforms other methods in terms of speed and accuracy. The average estimation errors for crack location and depth are (0.06%, 0%) for the PSNM, however, (0.09%, 0%), (0.46%, 0.54%) and (0.39%, 1.66%) for the GANM, the PSO and the NN methods, respectively. To validate the proposed method and investigate the modeling and measurement errors some experimental results are also included. The average values of experimental location and depth estimation errors are (9.24%, 8.56%) for the PSNM, but (9.64%, 9.50%), (10.89%, 10.89%), (11.53%, 11.64%) for the GANM, the PSO and the NN methods, respectively.
crack detection natural frequency particle swarm optimization neldermead simplex structural health monitoring