Dimensionality Reduction in Structural Health Monitoring: A Case Study on Damaged Wind Turbine Blades

In recent years, advances in structural health monitoring (SHM) of composite materials have been observed, driven by the adoption of intelligent diagnostic techniques. Vibration signals, which embed valuable structural health information, have been increasingly utilized in SHM. However, the high dimensionality of this data has necessitated significant computational resources and has made the differentiation between health states more complex. Highlighting the need for effective feature extraction and dimensionality reduction (DR), a study is conducted. In this research, advanced DR techniques, i.e., principal component analysis (PCA), locally linear embedding (LLE), t-distributed stochastic neighbors embedding (t-SNE), and uniform manifold approximation and projection (UMAP), are applied to an experimental dataset from a wind turbine blade under various health and environmental conditions (controlled-environment vibration signals). The extracted features are then processed for the classification phase, and it is found that UMAP provides the best performance, albeit with a slightly increased computational demand. The findings from this research offer invaluable insights for researchers and engineers, assisting in the selection of the most appropriate DR method for SHM, and weighing up accuracy against computational time.