Vibration-Adaption Deep Convolutional Transfer Learning Method for Stranded Wire Structural Health Monitoring Using Guided Wave

External vibration is the main disturbance condition in the practical monitoring of outdoor stranded structure using laser ultrasonic guided wave (UGW). It is difficult to extract and identify the real damage state under different vibration conditions due to the variation of the guided-wave feature distribution. At present, there is no effective solution to this practical problem. In this article, a new deep cross-domain adaptive semisupervised damage identification method is proposed by using transfer learning method and combining with the actual demand of stranded guided-wave monitoring. First, a novel laser excitation-piezoelectric receiving sensing method is realized by taking full advantage of the noncontact characteristics, wide frequency band, and high stability of the laser and piezoelectric sensors. Second, a multilayer convolutional neural network (CNN) is constructed to extract the damage features of the guided-wave signals in the source domain and map them to the high-level hidden space. Then, a multicore maximum mean discrepancy (MMD) method is designed to reduce the distribution difference of damage features between the target and source domains by using the optimal multicore selection method, and the essential damage features of UGWs were learned. Finally, different damage states of the target domain are effectively identified by feature identification. The experimental results illustrate that the proposed method can realize automatic extraction of inherent damage features and adaptive matching of multilayer features, connect the source and target domains in the high-level feature space, and learn the invariant features of guided-wave signals under different vibrations. Moreover, the proposed method has a good performance both in the mean between-class average distance and the mean within-class average distance damage degree of feature under various vibration conditions, reaches 100% accuracy in damage degree identification under different vibration conditions, and shows better performance than the comparison methods.