Generative Adversarial Networks for Labelled Vibration Data Generation
This provides a solution for generating labelled vibration data to aid structural damage diagnostics in civil engineering, though it is incremental as it adapts existing GAN methods to a specific domain.
The authors tackled the challenge of expensive and difficult vibration data collection for structural health monitoring by introducing a 1D W-DCGAN model based on GANs, DCNN, and Wasserstein distance to generate artificial labelled vibration data, successfully producing data very similar to the input.
As Structural Health Monitoring (SHM) being implemented more over the years, the use of operational modal analysis of civil structures has become more significant for the assessment and evaluation of engineering structures. Machine Learning (ML) and Deep Learning (DL) algorithms have been in use for structural damage diagnostics of civil structures in the last couple of decades. While collecting vibration data from civil structures is a challenging and expensive task for both undamaged and damaged cases, in this paper, the authors are introducing Generative Adversarial Networks (GAN) that is built on the Deep Convolutional Neural Network (DCNN) and using Wasserstein Distance for generating artificial labelled data to be used for structural damage diagnostic purposes. The authors named the developed model 1D W-DCGAN and successfully generated vibration data which is very similar to the input. The methodology presented in this paper will pave the way for vibration data generation for numerous future applications in the SHM domain.