On the use of synthetic data for SHM

Abstract

Machine learning has been successfully applied to many structural health monitoring (SHM) projects. However, it relies heavily on data from structures. Particularly, if supervised learning approaches are employed, then data from all possible damaged states of the structure will be required. For inexpensive structures, destructive means of acquiring those data under laboratory conditions may be possible, but for more expensive structures it may become prohibitively expensive, and other approaches will be required. Recently, generative machine learning models have been used to create synthetic data to create or augment databases and provide an alternative solution to the lack of training data. The current paper explores the use of generative adversarial networks (GANs) for the creation of synthetic data from different damaged states and their suitability for SHM. The approach is applied to a laboratory structure, a nonlinear Brake-Reuß beam where the damage scenarios correspond to different torque settings in the bolts of a lap-joint.