Enhancing structural health monitoring with machine learning and data surrogates: a TCA-based approach for damage detection and localisation
Battu, RS; Agathos, K; Papatheou, E
Date: 7 December 2023
Conference paper
Publisher
DEStech Publications, Inc.
Publisher DOI
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 ...
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 nu merical 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.
Management
Faculty of Environment, Science and Economy
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