Damage detection of offshore jacket structure using dynamic responses based on simulated model, intact state of real model and deep auto-encoder neural network

Since the maintenance and repairing costs of mechanical systems, such as structures and rotating machines are significantly high, one way to reduce these costs is to consider some approaches before any operational work to check for damages in such systems. In this study, a new method is presented for damage detection of offshore jacket structures in the presence of various uncertainties, such as modeling errors, measurement errors and environmental noises, based on the simulated model and intact state of the real model. In the proposed method, real intact structure data is used to update the simulated model parameters. Some parts of the signal that are not related to the nature of the system are removed using the complete ensemble empirical mode decomposition method. Frequency data is extracted from the vibrational signals using the frequency domain decomposition method. A deep auto-encoder neural network is designed to learn the damage-sensitive features from the frequency data and to damage detection of the structure. In order to train the proposed deep network, frequency data of the simulated model and real intact state are used; then the frequency data of the real structure is used to test the proposed deep network. The results show that the proposed method is capable for damage detection of the offshore jacket structure with more accurate results than the other comparative methods.