City Research Online

Abstract

This study investigates vibration‐based structural health monitoring (SHM) of a small‐scale wind turbine blade using multiple sensor types to evaluate the performance of an output‐only, data‐driven and semisupervised damage detection framework. The primary objective is to assess how different sensing modalities influence damage detection and localisation and to demonstrate the robustness of the proposed SHM approach across diverse sensor configurations in a wind turbine composite material blade. To this end, experimental accelerometer and strain‐gauge time series are considered in this study. The method integrates multivariate autoregressive (MAR) modelling, principal component analysis (PCA), Mahalanobis distance‐based dissimilarity metrics, and K‐means clustering. This hybrid framework requires no labelled damage data and minimal user input, offering a transparent, interpretable and computationally efficient solution suitable for real‐world applications. The blade was experimentally tested under progressive damage, which was introduced as sequential cracks at three locations, and under varying temperature conditions. Results show that the method reliably detects damage across sensor configurations, highlighting its potential as a practical and cost‐effective tool for continuous SHM. Notably, both sensor types were able to detect damage using only one sensor as input in the model; however, strain gauges proved more effective than accelerometers for damage localisation.

Publication Type:	Article
Additional Information:	© The Authors, 2026. Published by Wiley. This is an open-access article distributed under the terms of Creative Commons: Attribution International Public License 4.0 (http://creativecommons.org/licenses/by/4.0/).
Publisher Keywords:	damage detection, data-driven methods, semisupervised learning, sensor modalities, structural health monitoring
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery
Departments:	School of Science & Technology School of Science & Technology > Department of Engineering
SWORD Depositor:	Symplectic Administrator

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