Similarity Analysis to Enhance Transfer Learning for Damage Detection

One significant challenge in machine learning for Structural Health Monitoring (SHM) is reusing previously trained classifiers. A classifier might be suitable for one situation but not for another. Transfer learning techniques try to overcome this difficulty. In SHM, it is common to use the modal parameters as features; however, they are highly influenced by boundary conditions, geometry, and the level of structural damage. This work proposes an innovative approach that performs a similarity analysis to select features before applying transfer learning, aiming at improving classification and damage detection. The reasoning is that a higher similarity leads to a more efficient transfer of learning and, consequently, a better classification. Transfer learning is conducted via the domain adaptation technique known as Transfer Component Analysis (TCA), and cases with low similarity are compared to those with high similarity. Two datasets are analyzed. The first consists of a beam under different boundary conditions, and data are generated through numerical simulations. The second derives from an experimental setup of bolted joints with loosening damage. The proposed strategy, which uses a cosine-type similarity, is shown to improve the transfer learning classification.