Deep Learning Methods for Vibration-Based Structural Health Monitoring: A Review

The vibration signal is an effective diagnostic tool in structural health monitoring (SHM) fields that is closely related to abnormal states. Deep learning methods have got remarkable success in utilizing vibration signals for damage detection. This paper presents a systematic review of deep learning methods for SHM, focusing on the utilization of vibration signal data from different model perspectives. In recent years, there has been a significant increase in research on deep learning for vibration-based SHM. The accuracy of such works is equivalent to that of traditional machine learning approaches, and better results could be achieved by integrating multiple approaches. Furthermore, we found that transfer learning methods yield promising results when limited data are available to train the model. This paper aims to comprehensively review deep learning research on health monitoring using vibration signal data from multiple perspectives, with a particular emphasis on transfer learning methods for SHM. It fills the gap that existing reviews lack in the discussion of transfer learning for SHM. Finally, we analyze the challenges faced by current research and provide recommendations for future work. © 2023, The Author(s), under exclusive licence to Shiraz University.