Time series analysis and novelty detection are effective and promising methods for data-driven structural health monitoring (SHM) based on the statistical pattern recognition paradigm. However, processing substantially large volumes of vibration measurements may represent a serious limitation, especially for long-term SHM programs of large-scale civil structures. Moreover, shortcomings like the choice of an appropriate time series model in an automatic manner, the determination of optimal orders of the identified model and the classification of random high-dimensional features for damage detection, can strongly affect the performance of these approaches. This study is intended to propose statistical pattern recognition methods regarding time series modeling for feature extraction and novelty detection in feature classification in the presence of big data. These methods include an automatic model identification algorithm, an improved order determination approach and a hybrid distancebased novelty detection through a combination of Partition-based Kullback-Leibler divergence and Mahalanobissquared distance. Experimental datasets relevant to a cable-stayed bridge are considered to validate the effectiveness of the proposed methods. Results demonstrate that: the AutoRegressive-AutoRegressive with eXogenous input (AR-ARX) model turns out to be the most suitable representation for feature extraction; the orders of this model are efficiently and automatically determined; the proposed novelty detection approach is highly successful in detecting damage, even in case of large volumes of random high-dimensional features.

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