Research on Feature Extraction and De-noising Method of Vibration Signals of Deep-water Cages
Aiming at the parameter selection problem that the traditional variational modal decomposition relies heavily on the modal order and quadratic penalty factor when dealing with vibration signals of deep-sea cages,a variational modal decomposition(VMD)method based on the wolf group optimization algorithm is proposed.By introducing the wolf pack optimization algorithm and the optimal decomposition of the fitness function,the shortcoming of modal loss or aliasing caused by the variational modal decomposition when the parameters are set incorrectly is overcome.And the noise reduction and optimal feature extraction of vibration signals in deep-water cages without prior knowledge are realized.A 4-DOF numerical system is introduced to verify the correctness and effectiveness of the proposed method.Then,the measured data of a seated deep-water cage is used to extract structural feature information and reduce the signal noise.The results show that the proposed method can select the optimal modal parameters and penalty factors so as to realize accurate feature extraction of signals,which verifies the effectiveness and practicability of the method for safety monitoring of seated deep-water cage structures.
万方数据
维普
