Enhancing Structural Health Monitoring with Machine Learning and Data Surrogates

Abstract

Structural health monitoring (SHM) involves constantly monitoring the condition of structures to detect any damage or deterioration that might develop over time. Machine learning methods have been successfully used in SHM, however, their effectiveness is often limited by the availability of data for various damage cases. Such data can be especially hard to obtain from high-value structures. In this paper, transfer component analysis (TCA) with domain adaptation is utilised in conjunction with high-fidelity numerical models to generate surrogates for damage identification without the requirement for high volumes of data from various damaged states of the structure. The approach is demonstrated on a laboratory structure, a nonlinear Brake-Reuß beam, where damage scenarios correspond to different torque settings on a lap joint. It is shown that, in a three-class scenario, machine learning algorithms can be trained using numerical data and tested successfully on experimental data.