Data-driven damage localization is a demanding process for vibration-based structural health monitoring (SHM) strategies. The ability to locate single and multiple damage states featuring different severity levels, particularly the smaller ones, plays a prominent role in establishing an effective and robust method for damage assessment. The statistical pattern recognition paradigm based on feature extraction and statistical decision-making can be a successful framework for this process [1,2]. In case of data gathered by dense sensor networks, which provide vibration datasets of high dimensionality and large volume, this framework may be time-consuming or complex; it also results questionable whether existing order selection techniques are able to provide a low-order feature extraction approach [1]. Furthermore, a fast decision-making process within an unsupervised learning strategy can help overcome the main obstacles experienced in the accurate localization of damage, if large volumes of damage-sensitive features are extracted from high-dimensional samples gathered by the mentioned dense sensor networks. In this study, we propose an efficient unsupervised learning method for feature extraction by an iterative approach based on order reduction in AutoRegressive (AR) modelling [3], and for damage localization through a statistical distance method termed Kullback-Leibler Divergence with Empirical Probability Measure (KLDEPM).

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