A hybrid modeling strategy for training data generation in machine learning-based structural health monitoring

Concerning the cost-and resource-saving maintenance of assembly products, it is vital to detect any potential malfunctions, defects or structural damage at the earliest-possible stage. For this reason, considerable efforts are being put into the development of Structural Health Monitoring, a field encompassing different approaches to damage identification and capable of preventing defects and even failure. Structural Health Monitoring is often supported by machine learning, a tool for rapid and effective damage identification that can recognize patterns or changes in the data received from the structure. Despite the advances machine learning has made in recent years, obtaining a suitable data set for the efficient training of machine learning algorithms within Structural Health Monitoring remains a challenge. Currently, the data are usually obtained experimentally, with numerical or analytical models. However, the experimental approach can often be time consuming, while the reliability of numerically obtained data relies heavily on the accuracy of the numerical models in capturing the true behavior of the structure. Analytical models may be constrained by the complexity of the observed object. In this paper an alternative approach based on an experimental-numerical (i.e., hybrid) modeling approach is proposed to build a training set for Structural Health Monitoring. Frequency Based Substructuring is utilized to determine the response model of the assembled system based on the properties of its components as well as to mix experimental and numerical models, while leveraging the advantages of each. This makes it possible to generate the samples of the training set in the form of hybrid models of the structure of interest, exhibiting the realistic properties of a physical structure, with a reasonable measurement effort. Here, the approach is demonstrated for the process of joint-damage identification.