Vibration‐based detection and classification of structural changes using principal component analysis and t‐distributed stochastic neighbor embedding

• Published in: Structural control and health monitoring Vol. 27; no. 6
• Main Authors: Agis, David, Tibaduiza, Diego A., Pozo, Francesc
• Format: Journal Article
• Language: English
• Published: Pavia Wiley Subscription Services, Inc 01-06-2020
• Subjects: Aluminum; Centroids; Change detection; Classification; Clusters; detection; Embedding; Metal plates; Performance evaluation; Piezoelectric transducers; Piezoelectricity; principal component analysis (PCA); Principal components analysis; Sensors; Stochasticity; structural changes; Structural health monitoring; structural health monitoring (SHM); Transducers; t‐distributed stochastic neighbor embedding (t‐SNE); Vibration; Vibration analysis
• ISSN: 1545-2255; 1545-2263
• DOI: 10.1002/stc.2533

Summary
This paper describes a structural health monitoring strategy to detect and classify structural changes in structures that can be equipped with sensors. The proposed approach is based on the
t‐distributed stochastic neighbor embedding (
t‐SNE), a nonlinear technique that can represent the local structure of high‐dimensional data collected from multiple sensors in a plane or spatial representation. We propose the following basic steps for the detection and classification. First, the raw data are preprocessed: We scale the data using the mean‐centered group scaling and apply principal component analysis to reduce the dimensionality of the scaled data. Second,
t‐SNE is applied to represent the scaled and reduced data as points in a plane, defining a cluster for each structural state. Finally, the current structure to be diagnosed is associated with a cluster (or structural state) using three different strategies: (a) the smallest point‐centroid distance; (b) the majority voting; and (c) the sum of the inverse distances. The combination of
t‐SNE with our preprocessing and the three proposed classification strategies significantly improves the quality of the clusters that represent different structural states. We evaluate the performance of our method using experimental data from an aluminum plate instrumented with piezoelectric transducers. Results are presented in the time domain, and they reveal the high classification accuracy and strong performance of this method, with a percentage of correct decisions close to
100% in several scenarios.